Prof. Dr. Cyrill Stachniss

Head
Contact:
Email: cyrill.stachniss@nulligg.uni-bonn.de
Tel: +49 - 228 - 73 - 27 13 (secretary)
Tel: +49 - 228 - 73 - 27 14 (direct)
Fax: +49 - 228 - 73 - 27 12
Office: Nussallee 15, 1. OG, room 1.009
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn
Download CV
Google Scholar Profile
YouTube Channel

Research Interests

  • Probabilistic Robotics
  • Localization, Mapping, SLAM, Bundle Adjustment
  • Autonomous Navigation and Exploration
  • Visual and Laser Perception
  • Scene Analysis and Classification
  • Robotics for Agriculture
  • Unmanned Aerial Vehicles (UAVs and MAVs)
  • Autonomous Cars, Logistics, Wheeled Robots, …

Short CV

Cyrill Stachniss is a full professor for photogrammetry at the University of Bonn and recently became the deputy managing director of the Institute of Geodesy and Geoinformation. Before working in Bonn, he was a lecturer at the University of Freiburg in Germany, a guest lecturer at the University of Zaragoza in Spain, and a senior researcher at the Swiss Federal Institute of Technology in the group of Roland Siegwart. Cyrill Stachniss finished his habilitation in 2009 and received his PhD thesis entitled “Exploration and Mapping with Mobile Robots” supervised by Wolfram Burgard at the University of Freiburg in 2006. From 2008-2013, he was an associate editor of the IEEE Transactions on Robotics, since 2010 a Microsoft Research Faculty Fellow, and received the IEEE RAS Early Career Award in 2013. Since 2015, he is a senior editor for the IEEE Robotics and Automation Letters.

In his research, he focuses on probabilistic techniques in the context of mobile robotics, navigation problems, and visual perception. A central area of his research is autonomous exploration in combination with solutions to the simultaneous localization and mapping problem. He is also interested in classification and learning approaches, in computer controlled cars, and unmanned aerial vehicles.

Download extended CV

Awards

  • IROS 2017 — Best Application Paper Finalist (2017)
  • ICRA 2017 — Best Automation Paper Award by the IEEE Robotics and Automation Society (2017)
  • ICRA 2015 — Finalist Best Service Robotics paper (2015)
  • Faculty Teaching Award of the Faculty of Engineering, Freiburg Univ. (Fakultätslehrpreis) (2012/2013).
  • IEEE Robotics and Automation Society Early Career Award (2013).
  • ICRA 2013 — Best Associate Editor Award (2013).
  • ICRA 2013 — Finalist best student paper (2013)
    for the paper Robust Map Optimization Using Dynamic Covariance Scaling.
  • Robotics: Science and Systems Early Career Spotlight (2012).
  • Microsoft Research Faculty Fellow (2010).
  • 7th EURON Georges Giralt Award for the best robotics thesis in Europe in 2006 (received in 2008).
  • Wolfgang-Gentner PhD Award (2006)
    for my PhD thesis Exploration and Mapping with Mobile Robots .
  • ICRA 2005 — Finalist best student paper (2005)
    for the paper Supervised Learning of Places from Range Data using AdaBoost.
  • ICASE-IROS 2004 — Best paper award on application (2005)
    for the paper Grid-based FastSLAM and Exploration with Active Loop Closing.
  • Award of the German Engeneering Society, VDI (2003) for my master’s thesis Zielgerichtete Kollisionsvermeidung fuer mobile Roboter in dynamischen Umgebungen.

Slidesets for Courses

Publications

2017

  • C. Beekmans, J. Schneider, T. Laebe, M. Lennefer, C. Stachniss, and C. Simmer, “3D-Cloud Morphology and Motion from Dense Stereo for Fisheye Cameras,” in In Proceedings of the European Geosciences Union General Assembly (EGU) , 2017.
    [BibTeX]
    @InProceedings{beekmans17egu,
    Title = {3D-Cloud Morphology and Motion from Dense Stereo for Fisheye Cameras},
    Author = {Ch. Beekmans and J. Schneider and T. Laebe and M. Lennefer and C. Stachniss and C. Simmer},
    Booktitle = {In Proceedings of the European Geosciences Union General Assembly (EGU)},
    Year = {2017}
    }

  • I. Bogoslavskyi and C. Stachniss, “Efficient Online Segmentation for Sparse 3D Laser Scans,” PFG — Journal of Photogrammetry, Remote Sensing and Geoinformation Science, pp. 1-12, 2017.
    [BibTeX] [PDF]
    The ability to extract individual objects in the scene is key for a large number of autonomous navigation systems such as mobile robots or autonomous cars. Such systems navigating in dynamic environments need to be aware of objects that may change or move. In most perception cues, a pre-segmentation of the current image or laser scan into individual objects is the first processing step before a further analysis is performed. In this paper, we present an effective method that first removes the ground from the scan and then segments the 3D data in a range image representation into different objects. A key focus of our work is a fast execution with several hundred Hertz. Our implementation has small computational demands so that it can run online on most mobile systems. We explicitly avoid the computation of the 3D point cloud and operate directly on a 2.5D range image, which enables a fast segmentation for each 3D scan. This approach can furthermore handle sparse 3D data well, which is important for scanners such as the new Velodyne VLP-16 scanner. We implemented our approach in C++ and ROS, thoroughly tested it using different 3D scanners, and will release the source code of our implementation. Our method can operate at frame rates that are substantially higher than those of the sensors while using only a single core of a mobile CPU and producing high-quality segmentation results.

    @Article{bogoslavskyi17pfg,
    Title = {Efficient Online Segmentation for Sparse 3D Laser Scans},
    Author = {Bogoslavskyi, Igor and Stachniss, Cyrill},
    Journal = {PFG -- Journal of Photogrammetry, Remote Sensing and Geoinformation Science},
    Year = {2017},
    Pages = {1--12},
    Abstract = {The ability to extract individual objects in the scene is key for a large number of autonomous navigation systems such as mobile robots or autonomous cars. Such systems navigating in dynamic environments need to be aware of objects that may change or move. In most perception cues, a pre-segmentation of the current image or laser scan into individual objects is the first processing step before a further analysis is performed. In this paper, we present an effective method that first removes the ground from the scan and then segments the 3D data in a range image representation into different objects. A key focus of our work is a fast execution with several hundred Hertz. Our implementation has small computational demands so that it can run online on most mobile systems. We explicitly avoid the computation of the 3D point cloud and operate directly on a 2.5D range image, which enables a fast segmentation for each 3D scan. This approach can furthermore handle sparse 3D data well, which is important for scanners such as the new Velodyne VLP-16 scanner. We implemented our approach in C++ and ROS, thoroughly tested it using different 3D scanners, and will release the source code of our implementation. Our method can operate at frame rates that are substantially higher than those of the sensors while using only a single core of a mobile CPU and producing high-quality segmentation results.},
    Url = {http://link.springer.com/article/10.1007/s41064-016-0003-y}
    }

  • F. Liebisch, M. Popovic, J. Pfeifer, R. Khanna, P. Lottes, C. Stachniss, A. Pretto, I. S. Kyu, J. Nieto, R. Siegwart, and A. Walter, “Automatic UAV-based field inspection campaigns for weeding in row crops,” in Proceedings of the 10th EARSeL SIG Imaging Spectroscopy Workshop , 2017.
    [BibTeX]
    @InProceedings{liebisch17earsel,
    Title = {Automatic UAV-based field inspection campaigns for weeding in row crops},
    Author = {F. Liebisch and M. Popovic and J. Pfeifer and R. Khanna and P. Lottes and C. Stachniss and A. Pretto and S. In Kyu and J. Nieto and R. Siegwart and A. Walter},
    Booktitle = {Proceedings of the 10th EARSeL SIG Imaging Spectroscopy Workshop},
    Year = {2017}
    }

  • N. Chebrolu, P. Lottes, A. Schaefer, W. Winterhalter, W. Burgard, and C. Stachniss, “Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields,” The International Journal of Robotics Research, 2017. doi:10.1177/0278364917720510
    [BibTeX] [PDF]
    @Article{chebrolu2017ijrr,
    Title = {Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields},
    Author = {N. Chebrolu and P. Lottes and A. Schaefer and W. Winterhalter and W. Burgard and C. Stachniss},
    Journal = ijrr,
    Year = {2017},
    Doi = {10.1177/0278364917720510},
    Url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chebrolu2017ijrr.pdf}
    }

  • P. Lottes, R. Khanna, J. Pfeifer, R. Siegwart, and C. Stachniss, “UAV-Based Crop and Weed Classification for Smart Farming,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2017.
    [BibTeX] [PDF]
    @InProceedings{lottes17icra,
    Title = {UAV-Based Crop and Weed Classification for Smart Farming},
    Author = {P. Lottes and R. Khanna and J. Pfeifer and R. Siegwart and C. Stachniss},
    Booktitle = ICRA,
    Year = {2017},
    Url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/lottes17icra.pdf}
    }

  • P. Lottes and C. Stachniss, “Semi-Supervised Online Visual Crop and Weed Classification in Precision Farming Exploiting Plant Arrangement,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2017.
    [BibTeX] [PDF]
    @InProceedings{lottes17iros,
    Title = {Semi-Supervised Online Visual Crop and Weed Classification in Precision Farming Exploiting Plant Arrangement},
    Author = {P. Lottes and C. Stachniss},
    Booktitle = IROS,
    Year = {2017},
    Url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/lottes17iros.pdf}
    }

  • C. Merfels and C. Stachniss, “Sensor Fusion for Self-Localisation of Automated Vehicles,” PFG — Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2017.
    [BibTeX] [PDF]
    @Article{merfels17pfg,
    Title = {Sensor Fusion for Self-Localisation of Automated Vehicles},
    Author = {Merfels, C. and Stachniss, C.},
    Journal = {PFG -- Journal of Photogrammetry, Remote Sensing and Geoinformation Science},
    Year = {2017},
    Url = {http://link.springer.com/article/10.1007/s41064-017-0008-1}
    }

  • A. Milioto, P. Lottes, and C. Stachniss, “Real-time Blob-wise Sugar Beets vs Weeds Classification for Monitoring Fields using Convolutional Neural Networks,” in Proceedings of the ISPRS Conference on Unmanned Aerial Vehicles in Geomatics (UAV-g) , 2017.
    [BibTeX] [PDF]
    UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.

    @InProceedings{milioto17uavg,
    Title = {Real-time Blob-wise Sugar Beets vs Weeds Classification for Monitoring Fields using Convolutional Neural Networks},
    Author = {A. Milioto and P. Lottes and C. Stachniss},
    Booktitle = uavg,
    Year = {2017},
    Abstract = {UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.},
    url = {http://www.ipb.uni-bonn.de/pdfs/milioto17uavg.pdf}
    }

  • A. Milioto, P. Lottes, and C. Stachniss, “Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs,” arXiv preprint:1709.06764, 2017.
    [BibTeX] [PDF]
    Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20Hz, and is suitable for online operation in the fields.

    @Article{milioto17arxiv,
    Author = {A. Milioto and P. Lottes and C. Stachniss},
    Title = {Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs},
    Year = {2017},
    Journal = {arXiv preprint:1709.06764},
    Abstract = {Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20Hz, and is suitable for online operation in the fields.},
    Url = {https://arxiv.org/abs/1709.06764}
    }

  • L. Nardi and C. Stachniss, “User Preferred Behaviors for Robot Navigation Exploiting Previous Experiences,” in Robotics and Autonomous Systems , 2017. doi:10.1016/j.robot.2017.08.014
    [BibTeX] [PDF]
    Industry demands flexible robots that are able to accomplish different tasks at different locations such as navigation and mobile manipulation. Operators often require mobile robots operating on factory floors to follow definite and predictable behaviors. This becomes particularly important when a robot shares the workspace with other moving entities. In this paper, we present a system for robot navigation that exploits previous experiences to generate predictable behaviors that meet user’s preferences. Preferences are not explicitly formulated but implicitly extracted from robot experiences and automatically considered to plan paths for the successive tasks without requiring experts to hard-code rules or strategies. Our system aims at accomplishing navigation behaviors that follow user’s preferences also to avoid dynamic obstacles. We achieve this by considering a probabilistic approach for modeling uncertain trajectories of the moving entities that share the workspace with the robot. We implemented and thoroughly tested our system both in simulation and on a real mobile robot. The extensive experiments presented in this paper demonstrate that our approach allows a robot for successfully navigating while performing predictable behaviors and meeting user’s preferences

    @InProceedings{nardi17jras,
    Title = {User Preferred Behaviors for Robot Navigation Exploiting Previous Experiences},
    Author = {L. Nardi and C. Stachniss},
    Booktitle = jras,
    Year = {2017},
    Doi = {10.1016/j.robot.2017.08.014},
    Abstract = {Industry demands flexible robots that are able to accomplish different tasks at different locations such as navigation and mobile manipulation. Operators often require mobile robots operating on factory floors to follow definite and predictable behaviors. This becomes particularly important when a robot shares the workspace with other moving entities. In this paper, we present a system for robot navigation that exploits previous experiences to generate predictable behaviors that meet user’s preferences. Preferences are not explicitly formulated but implicitly extracted from robot experiences and automatically considered to plan paths for the successive tasks without requiring experts to hard-code rules or strategies. Our system aims at accomplishing navigation behaviors that follow user’s preferences also to avoid dynamic obstacles. We achieve this by considering a probabilistic approach for modeling uncertain trajectories of the moving entities that share the workspace with the robot. We implemented and thoroughly tested our system both in simulation and on a real mobile robot. The extensive experiments presented in this paper demonstrate that our approach allows a robot for successfully navigating while performing predictable behaviors and meeting user’s preferences},
    Url = {http://www.ipb.uni-bonn.de/pdfs/nardi17jras.pdf}
    }

  • E. Palazzolo and C. Stachniss, “Information-Driven Autonomous Exploration for a Vision-Based MAV,” in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences , 2017.
    [BibTeX] [PDF]
    @InProceedings{palazzolo2017uavg,
    Title = {Information-Driven Autonomous Exploration for a Vision-Based MAV},
    Author = {E. Palazzolo and C. Stachniss},
    Booktitle = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
    Year = {2017},
    Url = {http://www.ipb.uni-bonn.de/pdfs/palazzolo2017uavg.pdf}
    }

  • E. Palazzolo and C. Stachniss, “Change Detection in 3D Models Based on Camera Images,” in 9th Workshop on Planning, Perception and Navigation for Intelligent Vehicles at the IEEE Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2017.
    [BibTeX] [PDF]
    @InProceedings{palazzolo2017irosws,
    Title = {Change Detection in 3D Models Based on Camera Images},
    Author = {E. Palazzolo and C. Stachniss},
    Booktitle = {9th Workshop on Planning, Perception and Navigation for Intelligent Vehicles at the IEEE } # IROS,
    Year = {2017},
    Url = {http://www.ipb.uni-bonn.de/pdfs/palazzolo2017irosws}
    }

  • J. Schneider, C. Stachniss, and W. Förstner, “On the Quality and Efficiency of Approximate Solutions to Bundle Adjustment with Epipolar and Trifocal Constraints,” in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences , 2017, pp. 81-88. doi:10.5194/isprs-annals-IV-2-W3-81-2017
    [BibTeX] [PDF]
    Bundle adjustment is a central part of most visual SLAM and Structure from Motion systems and thus a relevant component of UAVs equipped with cameras. This paper makes two contributions to bundle adjustment. First, we present a novel approach which exploits trifocal constraints, i.e., constraints resulting from corresponding points observed in three camera images, which allows to estimate the camera pose parameters without 3D point estimation. Second, we analyze the quality loss compared to the optimal bundle adjustment solution when applying different types of approximations to the constrained optimization problem to increase efficiency. We implemented and thoroughly evaluated our approach using a UAV performing mapping tasks in outdoor environments. Our results indicate that the complexity of the constraint bundle adjustment can be decreased without loosing too much accuracy.

    @InProceedings{schneider17uavg,
    Title = {On the Quality and Efficiency of Approximate Solutions to Bundle Adjustment with Epipolar and Trifocal Constraints},
    Author = {J. Schneider and C. Stachniss and W. F\"orstner},
    Booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
    Year = {2017},
    Pages = {81-88},
    Volume = {IV-2/W3},
    Abstract = {Bundle adjustment is a central part of most visual SLAM and Structure from Motion systems and thus a relevant component of UAVs equipped with cameras. This paper makes two contributions to bundle adjustment. First, we present a novel approach which exploits trifocal constraints, i.e., constraints resulting from corresponding points observed in three camera images, which allows to estimate the camera pose parameters without 3D point estimation. Second, we analyze the quality loss compared to the optimal bundle adjustment solution when applying different types of approximations to the constrained optimization problem to increase efficiency. We implemented and thoroughly evaluated our approach using a UAV performing mapping tasks in outdoor environments. Our results indicate that the complexity of the constraint bundle adjustment can be decreased without loosing too much accuracy.},
    Doi = {10.5194/isprs-annals-IV-2-W3-81-2017},
    Url = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W3/81/2017/isprs-annals-IV-2-W3-81-2017.pdf}
    }

  • O. Vysotska and C. Stachniss, “Improving SLAM by Exploiting Building Information from Publicly Available Maps and Localization Priors,” PFG — Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol. 85, iss. 1, pp. 53-65, 2017.
    [BibTeX] [PDF]
    @Article{vysotska17pfg,
    Title = {Improving SLAM by Exploiting Building Information from Publicly Available Maps and Localization Priors},
    Author = {Vysotska, O. and Stachniss, C.},
    Journal = {PFG -- Journal of Photogrammetry, Remote Sensing and Geoinformation Science},
    Year = {2017},
    Number = {1},
    Pages = {53-65},
    Volume = {85},
    Url = {http://link.springer.com/article/10.1007/s41064-017-0006-3}
    }

  • O. Vysotska and C. Stachniss, “Relocalization under Substantial Appearance Changes using Hashing,” in 9th Workshop on Planning, Perception and Navigation for Intelligent Vehicles at the IEEE Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2017.
    [BibTeX] [PDF] [Code]
    [none]
    @InProceedings{vysotska2017irosws,
    Title = {Relocalization under Substantial Appearance Changes using Hashing},
    Author = {O. Vysotska and C. Stachniss},
    Booktitle = {9th Workshop on Planning, Perception and Navigation for Intelligent Vehicles at the IEEE } # IROS,
    Year = {2017},
    Abstract = {[none]},
    Url = {http://www.ipb.uni-bonn.de/pdfs/vysotska2017irosws.pdf},
    CodeUrl = {https://github.com/Photogrammetry-Robotics-Bonn/vpr_relocalization}
    }

  • J. Jung, C. Stachniss, and C. Kim, “Automatic room segmentation of 3D laser data using morphological processing,” ISPRS International Journal of Geo-Information, 2017.
    [BibTeX] [PDF]
    @Article{jung2017ijgi,
    author = {J. Jung and C. Stachniss and C. Kim},
    title = {Automatic room segmentation of 3D laser data using morphological processing},
    journal = {ISPRS International Journal of Geo-Information},
    year = {2017},
    url = {http://www.mdpi.com/2220-9964/6/7/206},
    }

  • R. Schirmer, P. Biber, and C. Stachniss, “Efficient Path Planning in Belief Space for Safe Navigation,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2017.
    [BibTeX] [PDF]
    Robotic lawn-mowers are required to stay within a predefined working area, otherwise they may drive into a pond or on the street. This turns navigation and path planning into safety critical components. If we consider using SLAM techniques in that context, we must be able to provide safety guarantees in the presence of sensor/actuator noise and featureless areas in the environment. In this paper, we tackle the problem of planning a path that maximizes robot safety while navigating inside the working area and under the constraints of limited computing resources and cheap sensors. Our approach uses a map of the environment to estimate localizability at all locations, and it uses these estimates to search for a path from start to goal in belief space using an extended heuristic search algorithm. We implemented our approach using C++ and ROS and thoroughly tested it on simulation data recorded on eight different gardens, as well as on a real robot. The experiments presented in this paper show that our approach leads to short computation times and short paths while maximizing robot safety under certain assumptions.

    @InProceedings{schirmer17iros,
    author = {R. Schirmer and P. Biber and C. Stachniss},
    title = {Efficient Path Planning in Belief Space for Safe Navigation},
    booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = {2017},
    abstract = {Robotic lawn-mowers are required to stay within a predefined working area, otherwise they may drive into a pond or on the street. This turns navigation and path planning into safety critical components. If we consider using SLAM techniques in that context, we must be able to provide safety guarantees in the presence of sensor/actuator noise and featureless areas in the environment. In this paper, we tackle the
    problem of planning a path that maximizes robot safety while navigating inside the working area and under the constraints of limited computing resources and cheap sensors. Our approach uses a map of the environment to estimate localizability at all locations, and it uses these estimates to search for a path from
    start to goal in belief space using an extended heuristic search algorithm. We implemented our approach using C++ and ROS and thoroughly tested it on simulation data recorded on eight different gardens, as well as on a real robot. The experiments presented in this paper show that our approach leads to short
    computation times and short paths while maximizing robot safety under certain assumptions.},
    url = {http://www.ipb.uni-bonn.de/pdfs/schirmer17iros.pdf},
    }

2016

  • N. Abdo, C. Stachniss, L. Spinello, and W. Burgard, “Organizing Objects by Predicting User Preferences Through Collaborative Filtering,” The International Journal of Robotics Research, 2016.
    [BibTeX] [PDF]
    [none]
    @Article{abdo16ijrr,
    Title = {Organizing Objects by Predicting User Preferences Through Collaborative Filtering},
    Author = {N. Abdo and C. Stachniss and L. Spinello and W. Burgard},
    Journal = IJRR,
    Year = {2016},
    Note = {arXiv:1512.06362},
    Abstract = {[none]},
    Url = {http://arxiv.org/abs/1512.06362}
    }

  • C. Beekmans, J. Schneider, T. Läbe, M. Lennefer, C. Stachniss, and C. Simmer, “Cloud Photogrammetry with Dense Stereo for Fisheye Cameras,” Atmospheric Chemistry and Physics (ACP), vol. 16, iss. 22, pp. 14231-14248, 2016. doi:10.5194/acp-16-14231-2016
    [BibTeX] [PDF]
    We present a novel approach for dense 3-D cloud reconstruction above an area of 10 × 10 km2 using two hemispheric sky imagers with fisheye lenses in a stereo setup. We examine an epipolar rectification model designed for fisheye cameras, which allows the use of efficient out-of-the-box dense matching algorithms designed for classical pinhole-type cameras to search for correspondence information at every pixel. The resulting dense point cloud allows to recover a detailed and more complete cloud morphology compared to previous approaches that employed sparse feature-based stereo or assumed geometric constraints on the cloud field. Our approach is very efficient and can be fully automated. From the obtained 3-D shapes, cloud dynamics, size, motion, type and spacing can be derived, and used for radiation closure under cloudy conditions, for example. Fisheye lenses follow a different projection function than classical pinhole-type cameras and provide a large field of view with a single image. However, the computation of dense 3-D information is more complicated and standard implementations for dense 3-D stereo reconstruction cannot be easily applied. Together with an appropriate camera calibration, which includes internal camera geometry, global position and orientation of the stereo camera pair, we use the correspondence information from the stereo matching for dense 3-D stereo reconstruction of clouds located around the cameras. We implement and evaluate the proposed approach using real world data and present two case studies. In the first case, we validate the quality and accuracy of the method by comparing the stereo reconstruction of a stratocumulus layer with reflectivity observations measured by a cloud radar and the cloud-base height estimated from a Lidar-ceilometer. The second case analyzes a rapid cumulus evolution in the presence of strong wind shear.

    @Article{beekmans16acp,
    Title = {Cloud Photogrammetry with Dense Stereo for Fisheye Cameras},
    Author = {C. Beekmans and J. Schneider and T. L\"abe and M. Lennefer and C. Stachniss and C. Simmer},
    Journal = {Atmospheric Chemistry and Physics (ACP)},
    Year = {2016},
    Number = {22},
    Pages = {14231-14248},
    Volume = {16},
    Abstract = {We present a novel approach for dense 3-D cloud reconstruction above an area of 10 × 10 km2 using two hemispheric sky imagers with fisheye lenses in a stereo setup. We examine an epipolar rectification model designed for fisheye cameras, which allows the use of efficient out-of-the-box dense matching algorithms designed for classical pinhole-type cameras to search for correspondence information at every pixel. The resulting dense point cloud allows to recover a detailed and more complete cloud morphology compared to previous approaches that employed sparse feature-based stereo or assumed geometric constraints on the cloud field. Our approach is very efficient and can be fully automated. From the obtained 3-D shapes, cloud dynamics, size, motion, type and spacing can be derived, and used for radiation closure under cloudy conditions, for example.
    Fisheye lenses follow a different projection function than classical pinhole-type cameras and provide a large field of view with a single image. However, the computation of dense 3-D information is more complicated and standard implementations for dense 3-D stereo reconstruction cannot be easily applied.
    Together with an appropriate camera calibration, which includes internal camera geometry, global position and orientation of the stereo camera pair, we use the correspondence information from the stereo matching for dense 3-D stereo reconstruction of clouds located around the cameras.
    We implement and evaluate the proposed approach using real world data and present two case studies. In the first case, we validate the quality and accuracy of the method by comparing the stereo reconstruction of a stratocumulus layer with reflectivity observations measured by a cloud radar and the cloud-base height estimated from a Lidar-ceilometer. The second case analyzes a rapid cumulus evolution in the presence of strong wind shear.},
    Doi = {10.5194/acp-16-14231-2016},
    Url = {http://www.ipb.uni-bonn.de/pdfs/beekmans16acp.pdf}
    }

  • I. Bogoslavskyi, M. Mazuran, and C. Stachniss, “Robust Homing for Autonomous Robots,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2016.
    [BibTeX] [PDF]
    [none]
    @InProceedings{bogoslavskyi16icra,
    Title = {Robust Homing for Autonomous Robots},
    Author = {I. Bogoslavskyi and M. Mazuran and C. Stachniss},
    Booktitle = icra,
    Year = {2016},
    Abstract = {[none]},
    Url = {http://www.ipb.uni-bonn.de/pdfs/bogoslavskyi16icra.pdf}
    }

  • I. Bogoslavskyi and C. Stachniss, “Fast Range Image-Based Segmentation of Sparse 3D Laser Scans for Online Operation,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2016.
    [BibTeX] [PDF]
    [none]
    @InProceedings{bogoslavskyi16iros,
    Title = {Fast Range Image-Based Segmentation of Sparse 3D Laser Scans for Online Operation},
    Author = {I. Bogoslavskyi and C. Stachniss},
    Booktitle = iros,
    Year = {2016},
    Abstract = {[none]},
    Url = {http://www.ipb.uni-bonn.de/pdfs/bogoslavskyi16iros.pdf}
    }

  • F. Liebisch, J. Pfeifer, R. Khanna, P. Lottes, C. Stachniss, T. Falck, S. Sander, R. Siegwart, A. Walter, and E. Galceran, “Flourish — A robotic approach for automation in crop management,” in Proceedings of the Workshop für Computer-Bildanalyse und unbemannte autonom fliegende Systeme in der Landwirtschaft , 2016.
    [BibTeX] [PDF]
    @InProceedings{liebisch16wslw,
    Title = {Flourish -- A robotic approach for automation in crop management},
    Author = {F. Liebisch and J. Pfeifer and R. Khanna and P. Lottes and C. Stachniss and T. Falck and S. Sander and R. Siegwart and A. Walter and E. Galceran},
    Booktitle = {Proceedings of the Workshop f\"ur Computer-Bildanalyse und unbemannte autonom fliegende Systeme in der Landwirtschaft},
    Year = {2016},
    Timestamp = {2016.06.15},
    Url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/liebisch16cbaws.pdf}
    }

  • P. Lottes, M. Höferlin, S. Sander, M. Müter, P. Schulze-Lammers, and C. Stachniss, “An Effective Classification System for Separating Sugar Beets and Weeds for Precision Farming Applications,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2016.
    [BibTeX] [PDF]
    @InProceedings{lottes16icra,
    Title = {An Effective Classification System for Separating Sugar Beets and Weeds for Precision Farming Applications},
    Author = {P. Lottes and M. H\"oferlin and S. Sander and M. M\"uter and P. Schulze-Lammers and C. Stachniss},
    Booktitle = ICRA,
    Year = {2016},
    Timestamp = {2016.01.15},
    Url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/lottes16icra.pdf}
    }

  • P. Lottes, M. Höferlin, S. Sander, and C. Stachniss, “Effective Vision-based Classification for Separating Sugar Beets and Weeds for Precision Farming,” Journal of Field Robotics, 2016. doi:10.1002/rob.21675
    [BibTeX] [PDF]
    @Article{lottes16jfr,
    Title = {Effective Vision-based Classification for Separating Sugar Beets and Weeds for Precision Farming},
    Author = {Lottes, Philipp and H\"oferlin, Markus and Sander, Slawomir and Stachniss, Cyrill},
    Journal = {Journal of Field Robotics},
    Year = {2016},
    Doi = {10.1002/rob.21675},
    ISSN = {1556-4967},
    Timestamp = {2016.10.5},
    Url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/lottes16jfr.pdf}
    }

  • C. Merfels and C. Stachniss, “Pose Fusion with Chain Pose Graphs for Automated Driving,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2016.
    [BibTeX] [PDF]
    @InProceedings{merfels16iros,
    Title = {Pose Fusion with Chain Pose Graphs for Automated Driving},
    Author = {Ch. Merfels and C. Stachniss},
    Booktitle = iros,
    Year = {2016},
    Url = {http://www.ipb.uni-bonn.de/pdfs/merfels16iros.pdf}
    }

  • L. Nardi and C. Stachniss, “Experience-Based Path Planning for Mobile Robots Exploiting User Preferences,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2016. doi:10.1109/IROS.2016.7759197
    [BibTeX] [PDF]
    The demand for flexible industrial robotic solutions that are able to accomplish tasks at different locations in a factory is growing more and more. When deploying mobile robots in a factory environment, the predictability and reproducibility of their behaviors become important and are often requested. In this paper, we propose an easy-to-use motion planning scheme that can take into account user preferences for robot navigation. The preferences are extracted implicitly from the previous experiences or from demonstrations and are automatically considered in the subsequent planning steps. This leads to reproducible and thus better to predict navigation behaviors of the robot, without requiring experts to hard-coding control strategies or cost functions within a planner. Our system has been implemented and evaluated on a simulated KUKA mobile robot in different environments.

    @InProceedings{nardi16iros,
    Title = {Experience-Based Path Planning for Mobile Robots Exploiting User Preferences},
    Author = {L. Nardi and C. Stachniss},
    Booktitle = iros,
    Year = {2016},
    Doi = {10.1109/IROS.2016.7759197},
    Abstract = {The demand for flexible industrial robotic solutions that are able to accomplish tasks at different locations in a factory is growing more and more. When deploying mobile robots in a factory environment, the predictability and reproducibility of their behaviors become important and are often requested. In this paper, we propose an easy-to-use motion planning scheme that can take into account user preferences for robot navigation. The preferences are extracted implicitly from the previous experiences or from demonstrations and are automatically considered in the subsequent planning steps. This leads to reproducible and thus better to predict navigation behaviors of the robot, without requiring experts to hard-coding control strategies or cost functions within a planner. Our system has been implemented and evaluated on a simulated KUKA mobile robot in different environments.},
    Url = {http://www.ipb.uni-bonn.de/pdfs/nardi16iros.pdf}
    }

  • S. Osswald, M. Bennewitz, W. Burgard, and C. Stachniss, “Speeding-Up Robot Exploration by Exploiting Background Information,” IEEE Robotics and Automation Letters (RA-L) and IEEE International Conference on Robotics & Automation (ICRA), 2016.
    [BibTeX] [PDF]
    @Article{osswald16ral,
    Title = {Speeding-Up Robot Exploration by Exploiting Background Information},
    Author = {S. Osswald and M. Bennewitz and W. Burgard and C. Stachniss},
    Journal = {IEEE Robotics and Automation Letters (RA-L) and IEEE International Conference on Robotics \& Automation (ICRA)},
    Year = {2016},
    Url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/osswald16ral.pdf}
    }

  • D. Perea-Ström, I. Bogoslavskyi, and C. Stachniss, “Robust Exploration and Homing for Autonomous Robots,” in Robotics and Autonomous Systems , 2016.
    [BibTeX] [PDF]
    @InProceedings{perea16jras,
    Title = {Robust Exploration and Homing for Autonomous Robots},
    Author = {D. Perea-Str{\"o}m and I. Bogoslavskyi and C. Stachniss},
    Booktitle = jras,
    Year = {2016},
    Url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/perea16jras.pdf}
    }

  • J. Schneider, C. Eling, L. Klingbeil, H. Kuhlmann, W. Förstner, and C. Stachniss, “Fast and Effective Online Pose Estimation and Mapping for UAVs,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2016, pp. 4784-4791. doi:10.1109/ICRA.2016.7487682
    [BibTeX] [PDF]
    Online pose estimation and mapping in unknown environments is essential for most mobile robots. Especially autonomous unmanned aerial vehicles require good pose estimates at comparably high frequencies. In this paper, we propose an effective system for online pose and simultaneous map estimation designed for light-weight UAVs. Our system consists of two components: (1) real-time pose estimation combining RTK-GPS and IMU at 100 Hz and (2) an effective SLAM solution running at 10 Hz using image data from an omnidirectional multi-fisheye-camera system. The SLAM procedure combines spatial resection computed based on the map that is incrementally refined through bundle adjustment and combines the image data with raw GPS observations and IMU data on keyframes. The overall system yields a real-time, georeferenced pose at 100 Hz in GPS-friendly situations. Additionally, we obtain a precise pose and feature map at 10 Hz even in cases where the GPS is not observable or underconstrained. Our system has been implemented and thoroughly tested on a 5 kg copter and yields accurate and reliable pose estimation at high frequencies. We compare the point cloud obtained by our method with a model generated from georeferenced terrestrial laser scanner.

    @InProceedings{schneider16icra,
    Title = {Fast and Effective Online Pose Estimation and Mapping for UAVs},
    Author = {J. Schneider and C. Eling and L. Klingbeil and H. Kuhlmann and W. F\"orstner and C. Stachniss},
    Booktitle = icra,
    Year = {2016},
    Pages = {4784--4791},
    Abstract = {Online pose estimation and mapping in unknown environments is essential for most mobile robots. Especially autonomous unmanned aerial vehicles require good pose estimates at comparably high frequencies. In this paper, we propose an effective system for online pose and simultaneous map estimation designed for light-weight UAVs. Our system consists of two components: (1) real-time pose estimation combining RTK-GPS and IMU at 100 Hz and (2) an effective SLAM solution running at 10 Hz using image data from an omnidirectional multi-fisheye-camera system. The SLAM procedure combines spatial resection computed based on the map that is incrementally refined through bundle adjustment and combines the image data with raw GPS observations and IMU data on keyframes. The overall system yields a real-time, georeferenced pose at 100 Hz in GPS-friendly situations. Additionally, we obtain a precise pose and feature map at 10 Hz even in cases where the GPS is not observable or underconstrained. Our system has been implemented and thoroughly tested on a 5 kg copter and yields accurate and reliable pose estimation at high frequencies. We compare the point cloud obtained by our method with a model generated from georeferenced terrestrial laser scanner.},
    Doi = {10.1109/ICRA.2016.7487682},
    Url = {http://www.ipb.uni-bonn.de/pdfs/schneider16icra.pdf}
    }

  • J. Schneider, C. Stachniss, and W. Förstner, “Dichtes Stereo mit Fisheye-Kameras,” in UAV 2016 — Vermessung mit unbemannten Flugsystemen , 2016, pp. 247-264.
    [BibTeX]
    @InProceedings{schneider16dvw,
    Title = {Dichtes Stereo mit Fisheye-Kameras},
    Author = {J. Schneider and C. Stachniss and W. F\"orstner},
    Booktitle = {UAV 2016 -- Vermessung mit unbemannten Flugsystemen},
    Year = {2016},
    Pages = {247-264},
    Publisher = {Wi{\ss}ner Verlag},
    Series = {Schriftenreihe des DVW},
    Volume = {82}
    }

  • J. Schneider, C. Stachniss, and W. Förstner, “On the Accuracy of Dense Fisheye Stereo,” IEEE Robotics and Automation Letters (RA-L)and IEEE International Conference on Robotics & Automation (ICRA), vol. 1, iss. 1, pp. 227-234, 2016. doi:10.1109/LRA.2016.2516509
    [BibTeX] [PDF]
    Fisheye cameras offer a large field of view, which is important for several robotics applications as a larger field of view allows for covering a large area with a single image. In contrast to classical cameras, however, fisheye cameras cannot be approximated well using the pinhole camera model and this renders the computation of depth information from fisheye stereo image pairs more complicated. In this work, we analyze the combination of an epipolar rectification model for fisheye stereo cameras with existing dense methods. This has the advantage that existing dense stereo systems can be applied as a black-box even with cameras that have field of view of more than 180 deg to obtain dense disparity information. We thoroughly investigate the accuracy potential of such fisheye stereo systems using image data from our UAV. The empirical analysis is based on image pairs of a calibrated fisheye stereo camera system and two state-of-the-art algorithms for dense stereo applied to adequately rectified image pairs from fisheye stereo cameras. The canonical stochastic model for sensor points assumes homogeneous uncertainty and we generalize this model based on an empirical analysis using a test scene consisting of mutually orthogonal planes. We show (1) that the combination of adequately rectified fisheye image pairs and dense methods provides dense 3D point clouds at 6-7 Hz on our autonomous multi-copter UAV, (2) that the uncertainty of points depends on their angular distance from the optical axis, (3) how to estimate the variance component as a function of that distance, and (4) how the improved stochastic model improves the accuracy of the scene points.

    @Article{schneider16ral,
    Title = {On the Accuracy of Dense Fisheye Stereo},
    Author = {J. Schneider and C. Stachniss and W. F\"orstner},
    Journal = {IEEE Robotics and Automation Letters (RA-L)and IEEE International Conference on Robotics \& Automation (ICRA)},
    Year = {2016},
    Number = {1},
    Pages = {227-234},
    Volume = {1},
    Abstract = {Fisheye cameras offer a large field of view, which is important for several robotics applications as a larger field of view allows for covering a large area with a single image. In contrast to classical cameras, however, fisheye cameras cannot be approximated well using the pinhole camera model and this renders the computation of depth information from fisheye stereo image pairs more complicated. In this work, we analyze the combination of an epipolar rectification model for fisheye stereo cameras with existing dense methods. This has the advantage that existing dense stereo systems can be applied as a black-box even with cameras that have field of view of more than 180 deg to obtain dense disparity information. We thoroughly investigate the accuracy potential of such fisheye stereo systems using image data from our UAV. The empirical analysis is based on image pairs of a calibrated fisheye stereo camera system and two state-of-the-art algorithms for dense stereo applied to adequately rectified image pairs from fisheye stereo cameras. The canonical stochastic model for sensor points assumes homogeneous uncertainty and we generalize this model based on an empirical analysis using a test scene consisting of mutually orthogonal planes. We show (1) that the combination of adequately rectified fisheye image pairs and dense methods provides dense 3D point clouds at 6-7 Hz on our autonomous multi-copter UAV, (2) that the uncertainty of points depends on their angular distance from the optical axis, (3) how to estimate the variance component as a function of that distance, and (4) how the improved stochastic model improves the accuracy of the scene points.},
    Doi = {10.1109/LRA.2016.2516509},
    Url = {http://www.ipb.uni-bonn.de/pdfs/schneider16ral.pdf}
    }

  • T. Schubert, S. Wenzel, R. Roscher, and C. Stachniss, “Investigation of Latent Traces Using Infrared Reflectance Hyperspectral Imaging,” in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences , 2016, pp. 97-102. doi:10.5194/isprs-annals-III-7-97-2016
    [BibTeX] [PDF]
    The detection of traces is a main task of forensic science. A potential method is hyperspectral imaging (HSI) from which we expect to capture more fluorescence effects than with common Forensic Light Sources (FLS). Specimen of blood, semen and saliva traces in several dilution steps are prepared on cardboard substrate. As our key result we successfully make latent traces visible up to highest available dilution (1:8000). We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the Shortwave Infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood (ML) classifier assuming normally distributed data. Random Forest builds a competitive approach. The classifiers retrieve the exact positions of labeled trace preparation up to highest dilution and determine posterior probabilities. By modeling the classification with a Markov Random Field we obtain smoothed results.

    @InProceedings{Schubert2016Investigation,
    Title = {{Investigation of Latent Traces Using Infrared Reflectance Hyperspectral Imaging}},
    Author = {Schubert, Till and Wenzel, Susanne and Roscher, Ribana and Stachniss, Cyrill},
    Booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
    Year = {2016},
    Pages = {97--102},
    Volume = {III-7},
    Abstract = {The detection of traces is a main task of forensic science. A potential method is hyperspectral imaging (HSI) from which we expect to capture more fluorescence effects than with common Forensic Light Sources (FLS). Specimen of blood, semen and saliva traces in several dilution steps are prepared on cardboard substrate. As our key result we successfully make latent traces visible up to highest available dilution (1:8000). We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the Shortwave Infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood (ML) classifier assuming normally distributed data. Random Forest builds a competitive approach. The classifiers retrieve the exact positions of labeled trace preparation up to highest dilution and determine posterior probabilities. By modeling the classification with a Markov Random Field we obtain smoothed results.},
    Doi = {10.5194/isprs-annals-III-7-97-2016},
    Url = {http://www.ipb.uni-bonn.de/pdfs/Schubert2016Investigation.pdf}
    }

  • C. Siedentop, V. Laukhart, B. Krastev, D. Kasper, A. Wenden, G. Breuel, and C. Stachniss, “Autonomous Parking Using Previous Paths,” in Advanced Microsystems for Automotive Applications 2015: Smart Systems for Green and Automated Driving. Lecture Notes in Mobility., T. Schulze, B. Müller, and G. Meyer, Eds., Springer, 2016, pp. 3-14. doi:10.1007/978-3-319-20855-8_1
    [BibTeX]
    @InBook{siedentop16lnib,
    Title = {Autonomous Parking Using Previous Paths},
    Author = {C. Siedentop and V. Laukhart and B. Krastev and D. Kasper and A. Wenden and G. Breuel and C. Stachniss},
    Editor = {T. Schulze and B. M{\"u}ller and G. Meyer},
    Pages = {3-14},
    Publisher = {Springer},
    Year = {2016},
    Booktitle = {Advanced Microsystems for Automotive Applications 2015: Smart Systems for Green and Automated Driving. Lecture Notes in Mobility.},
    Doi = {10.1007/978-3-319-20855-8_1}
    }

  • C. Stachniss, “Springer Handbook of Robotics.” Springer, 2016.
    [BibTeX]
    [none]
    @InBook{springerbook-photo-slamchapter,
    Title = {Springer Handbook of Robotics},
    Author = {C. Stachniss},
    Chapter = {Simultaneous Localization and Mapping},
    Publisher = {Springer},
    Year = {2016},
    Abstract = {[none]},
    Timestamp = {2016.04.25}
    }

  • O. Vysotska and C. Stachniss, “Exploiting Building Information from Publicly Available Maps in Graph-Based SLAM,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2016.
    [BibTeX] [PDF]
    [none]
    @InProceedings{vysotska16iros,
    Title = {Exploiting Building Information from Publicly Available Maps in Graph-Based SLAM},
    Author = {O. Vysotska and C. Stachniss},
    Booktitle = iros,
    Year = {2016},
    Abstract = {[none]},
    Url = {http://www.ipb.uni-bonn.de/pdfs/vysotska16iros.pdf}
    }

  • O. Vysotska and C. Stachniss, “Lazy Data Association For Image Sequences Matching Under Substantial Appearance Changes,” IEEE Robotics and Automation Letters (RA-L)and IEEE International Conference on Robotics & Automation (ICRA), vol. 1, iss. 1, pp. 1-8, 2016. doi:10.1109/LRA.2015.2512936
    [BibTeX] [PDF] [Code] [Video]
    Localization is an essential capability for mobile robots and the ability to localize in changing environments is key to robust outdoor navigation. Robots operating over extended periods of time should be able to handle substantial appearance changes such as those occurring over seasons or under different weather conditions. In this letter, we investigate the problem of efficiently coping with seasonal appearance changes in online localization. We propose a lazy data association approach for matching streams of incoming images to a reference image sequence in an online fashion. We present a search heuristic to quickly find matches between the current image sequence and a database using a data association graph. Our experiments conducted under substantial seasonal changes suggest that our approach can efficiently match image sequences while requiring a comparably small number of image to image comparisons

    @Article{vysotska16ral,
    Title = {Lazy Data Association For Image Sequences Matching Under Substantial Appearance Changes},
    Author = {O. Vysotska and C. Stachniss},
    Journal = {IEEE Robotics and Automation Letters (RA-L)and IEEE International Conference on Robotics \& Automation (ICRA)},
    Year = {2016},
    Number = {1},
    Pages = {1-8},
    Volume = {1},
    Abstract = {Localization is an essential capability for mobile robots and the ability to localize in changing environments is key to robust outdoor navigation. Robots operating over extended periods of time should be able to handle substantial appearance changes such as those occurring over seasons or under different weather conditions. In this letter, we investigate the problem of efficiently coping with seasonal appearance changes in online localization. We propose a lazy data association approach for matching streams of incoming images to a reference image sequence in an online fashion. We present a search heuristic to quickly find matches between the current image sequence and a database using a data association graph. Our experiments conducted under substantial seasonal changes suggest that our approach can efficiently match image sequences while requiring a comparably small number of image to image comparisons},
    Doi = {10.1109/LRA.2015.2512936},
    Timestamp = {2016.04.18},
    Url = {http://www.ipb.uni-bonn.de/pdfs/vysotska16ral-icra.pdf},
    CodeUrl = {https://github.com/Photogrammetry-Robotics-Bonn/online_place_recognition},
    VideoUrl = {https://www.youtube.com/watch?v=l-hNk7Z4lSk},
    }

2015

  • N. Abdo, C. Stachniss, L. Spinello, and W. Burgard, “Collaborative Filtering for Predicting User Preferences for Organizing Objects,” arxiv–CoRR, vol. abs/1512.06362, 2015.
    [BibTeX] [PDF]
    [none]
    @Article{abdo15arxiv,
    Title = {Collaborative Filtering for Predicting User Preferences for Organizing Objects},
    Author = {N. Abdo and C. Stachniss and L. Spinello and W. Burgard},
    Journal = {arxiv--CoRR},
    Year = {2015},
    Note = {arXiv:1512.06362 [cs.RO]},
    Volume = {abs/1512.06362},
    Abstract = {[none]},
    Timestamp = {2016.04.18},
    Url = {http://arxiv.org/abs/1512.06362}
    }

  • N. Abdo, C. Stachniss, L. Spinello, and W.Burgard, “Robot, Organize my Shelves! Tidying up Objects by Predicting User Preferences,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2015, pp. 1557-1564. doi:10.1109/ICRA.2015.7139396
    [BibTeX] [PDF]
    As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, learning our preferences is a nontrivial problem, as many of them stem from a variety of factors including personal taste, cultural background, or common sense. Obviously, such factors are hard to formulate or model a priori. In this paper, we present a solution for tidying up objects in containers, e.g., shelves or boxes, by following user preferences. We learn the user preferences using collaborative filtering based on crowdsourced and mined data. First, we predict pairwise object preferences of the user. Then, we subdivide the objects in containers by modeling a spectral clustering problem. Our solution is easy to update, does not require complex modeling, and improves with the amount of user data. We evaluate our approach using crowdsoucing data from over 1,200 users and demonstrate its effectiveness for two tidy-up scenarios. Additionally, we show that a real robot can reliably predict user preferences using our approach.

    @InProceedings{abdo15icra,
    Title = {Robot, Organize my Shelves! Tidying up Objects by Predicting User Preferences},
    Author = {N. Abdo and C. Stachniss and L. Spinello and W.Burgard},
    Booktitle = ICRA,
    Year = {2015},
    Pages = {1557-1564},
    Abstract = {As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, learning our preferences is a nontrivial problem, as many of them stem from a variety of factors including personal taste, cultural background, or common sense. Obviously, such factors are hard to formulate or model a priori. In this paper, we present a solution for tidying up objects in containers, e.g., shelves or boxes, by following user preferences. We learn the user preferences using collaborative filtering based on crowdsourced and mined data. First, we predict pairwise object preferences of the user. Then, we subdivide the objects in containers by modeling a spectral clustering problem. Our solution is easy to update, does not require complex modeling, and improves with the amount of user data. We evaluate our approach using crowdsoucing data from over 1,200 users and demonstrate its effectiveness for two tidy-up scenarios. Additionally, we show that a real robot can reliably predict user preferences using our approach.},
    Doi = {10.1109/ICRA.2015.7139396},
    Url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/abdo15icra.pdf}
    }

  • I. Bogoslavskyi, L. Spinello, W. Burgard, and C. Stachniss, “Where to Park? Minimizing the Expected Time to Find a Parking Space,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2015, pp. 2147-2152. doi:10.1109/ICRA.2015.7139482
    [BibTeX] [PDF]
    Quickly finding a free parking spot that is close to a desired target location can be a difficult task. This holds for human drivers and autonomous cars alike. In this paper, we investigate the problem of predicting the occupancy of parking spaces and exploiting this information during route planning. We propose an MDP-based planner that considers route information as well as the occupancy probabilities of parking spaces to compute the path that minimizes the expected total time for finding an unoccupied parking space and for walking from the parking location to the target destination. We evaluated our system on real world data gathered over several days in a real parking lot. We furthermore compare our approach to three parking strategies and show that our method outperforms the alternative behaviors.

    @InProceedings{bogoslavskyi15icra,
    Title = {Where to Park? Minimizing the Expected Time to Find a Parking Space},
    Author = {I. Bogoslavskyi and L. Spinello and W. Burgard and C. Stachniss},
    Booktitle = ICRA,
    Year = {2015},
    Pages = {2147-2152},
    Abstract = {Quickly finding a free parking spot that is close to a desired target location can be a difficult task. This holds for human drivers and autonomous cars alike. In this paper, we investigate the problem of predicting the occupancy of parking spaces and exploiting this information during route planning. We propose an MDP-based planner that considers route information as well as the occupancy probabilities of parking spaces to compute the path that minimizes the expected total time for finding an unoccupied parking space and for walking from the parking location to the target destination. We evaluated our system on real world data gathered over several days in a real parking lot. We furthermore compare our approach to three parking strategies and show that our method outperforms the alternative behaviors.},
    Doi = {10.1109/ICRA.2015.7139482},
    Timestamp = {2015.06.29},
    Url = {http://www.ipb.uni-bonn.de/pdfs/bogoslavskyi15icra.pdf}
    }

  • T. Naseer, M. Ruhnke, L. Spinello, C. Stachniss, and W. Burgard, “Robust Visual SLAM Across Seasons,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2015, pp. 2529-2535. doi:10.1109/IROS.2015.7353721
    [BibTeX] [PDF]
    In this paper, we present an appearance-based visual SLAM approach that focuses on detecting loop closures across seasons. Given two image sequences, our method first extracts one descriptor per image for both sequences using a deep convolutional neural network. Then, we compute a similarity matrix by comparing each image of a query sequence with a database. Finally, based on the similarity matrix, we formulate a flow network problem and compute matching hypotheses between sequences. In this way, our approach can handle partially matching routes, loops in the trajectory and different speeds of the robot. With a matching hypothesis as loop closure information and the odometry information of the robot, we formulate a graph based SLAM problem and compute a joint maximum likelihood trajectory.

    @InProceedings{naseer15iros,
    Title = {Robust Visual SLAM Across Seasons},
    Author = {Naseer, Tayyab and Ruhnke, Michael and Spinello, Luciano and Stachniss, Cyrill and Burgard, Wolfram},
    Booktitle = iros,
    Year = {2015},
    Pages = {2529 - 2535},
    Abstract = {In this paper, we present an appearance-based visual SLAM approach that focuses on detecting loop closures across seasons. Given two image sequences, our method first extracts one descriptor per image for both sequences using a deep convolutional neural network. Then, we compute a similarity matrix by comparing each image of a query sequence with a database. Finally, based on the similarity matrix, we formulate a flow network problem and compute matching hypotheses between sequences. In this way, our approach can handle partially matching routes, loops in the trajectory and different speeds of the robot. With a matching hypothesis as loop closure information and the odometry information of the robot, we formulate a graph based SLAM problem and compute a joint maximum likelihood trajectory.},
    Doi = {10.1109/IROS.2015.7353721},
    Timestamp = {2016.04.19},
    Url = {http://www.ipb.uni-bonn.de/pdfs/Naseer2015Robust.pdf}
    }

  • D. Perea-Ström, F. Nenci, and C. Stachniss, “Predictive Exploration Considering Previously Mapped Environments,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2015, pp. 2761-2766. doi:10.1109/ICRA.2015.7139574
    [BibTeX] [PDF]
    The ability to explore an unknown environment is an important prerequisite for building truly autonomous robots. The central decision that a robot needs to make when exploring an unknown environment is to select the next view point(s) for gathering observations. In this paper, we consider the problem of how to select view points that support the underlying mapping process. We propose a novel approach that makes predictions about the structure of the environments in the unexplored areas by relying on maps acquired previously. Our approach seeks to find similarities between the current surroundings of the robot and previously acquired maps stored in a database in order to predict how the environment may expand in the unknown areas. This allows us to predict potential future loop closures early. This knowledge is used in the view point selection to actively close loops and in this way reduce the uncertainty in the robot’s belief. We implemented and tested the proposed approach. The experiments indicate that our method improves the ability of a robot to explore challenging environments and improves the quality of the resulting maps.

    @InProceedings{perea15icra,
    Title = {Predictive Exploration Considering Previously Mapped Environments},
    Author = {D. Perea-Str{\"o}m and F. Nenci and C. Stachniss},
    Booktitle = ICRA,
    Year = {2015},
    Pages = {2761-2766},
    Abstract = {The ability to explore an unknown environment is an important prerequisite for building truly autonomous robots. The central decision that a robot needs to make when exploring an unknown environment is to select the next view point(s) for gathering observations. In this paper, we consider the problem of how to select view points that support the underlying mapping process. We propose a novel approach that makes predictions about the structure of the environments in the unexplored areas by relying on maps acquired previously. Our approach seeks to find similarities between the current surroundings of the robot and previously acquired maps stored in a database in order to predict how the environment may expand in the unknown areas. This allows us to predict potential future loop closures early. This knowledge is used in the view point selection to actively close loops and in this way reduce the uncertainty in the robot's belief. We implemented and tested the proposed approach. The experiments indicate that our method improves the ability of a robot to explore challenging environments and improves the quality of the resulting maps.},
    Doi = {10.1109/ICRA.2015.7139574},
    Url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/perea15icra.pdf}
    }

  • C. Siedentop, R. Heinze, D. Kasper, G. Breuel, and C. Stachniss, “Path-Planning for Autonomous Parking with Dubins Curves,” in Proceedings of the Workshop Fahrerassistenzsysteme , 2015.
    [BibTeX]
    @InProceedings{siedentop15fas,
    Title = {Path-Planning for Autonomous Parking with Dubins Curves},
    Author = {C. Siedentop and R. Heinze and D. Kasper and G. Breuel and C. Stachniss},
    Booktitle = {Proceedings of the Workshop Fahrerassistenzsysteme},
    Year = {2015}
    }

  • O. Vysotska, T. Naseer, L. Spinello, W. Burgard, and C. Stachniss, “Efficient and Effective Matching of Image Sequences Under Substantial Appearance Changes Exploiting GPS Prior,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2015, pp. 2774-2779. doi:10.1109/ICRA.2015.7139576
    [BibTeX] [PDF]
    @InProceedings{vysotska15icra,
    Title = {Efficient and Effective Matching of Image Sequences Under Substantial Appearance Changes Exploiting GPS Prior},
    Author = {O. Vysotska and T. Naseer and L. Spinello and W. Burgard and C. Stachniss},
    Booktitle = ICRA,
    Year = {2015},
    Pages = {2774-2779},
    Doi = {10.1109/ICRA.2015.7139576},
    Timestamp = {2015.06.29},
    Url = {http://www.ipb.uni-bonn.de/pdfs/vysotska15icra.pdf}
    }

  • O. Vysotska and C. Stachniss, “Lazy Sequences Matching Under Substantial Appearance Changes,” in Workshop on Visual Place Recognition in Changing Environments at the IEEE Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2015.
    [BibTeX] [PDF]
    [none]
    @InProceedings{vysotska15icraws,
    Title = {Lazy Sequences Matching Under Substantial Appearance Changes},
    Author = {O. Vysotska and C. Stachniss},
    Booktitle = {Workshop on Visual Place Recognition in Changing Environments at the IEEE } # ICRA,
    Year = {2015},
    Abstract = {[none]},
    Timestamp = {2015.06.29},
    Url = {http://www.ipb.uni-bonn.de/pdfs/vysotska15icra-ws.pdf}
    }

2014

  • B. Frank, C. Stachniss, R. Schmedding, M. Teschner, and W. Burgard, “Learning object deformation models for robot motion planning,” Robotics and Autonomous Systems, p. -, 2014. doi:http://dx.doi.org/10.1016/j.robot.2014.04.005
    [BibTeX] [PDF]
    [none]
    @Article{Frank2014,
    Title = {Learning object deformation models for robot motion planning },
    Author = {Barbara Frank and Cyrill Stachniss and R\"{u}diger Schmedding and Matthias Teschner and Wolfram Burgard},
    Journal = {Robotics and Autonomous Systems },
    Year = {2014},
    Pages = { - },
    Abstract = {[none]},
    Crossref = {mn},
    Doi = {http://dx.doi.org/10.1016/j.robot.2014.04.005},
    ISSN = {0921-8890},
    Keywords = {Mobile robots},
    Url = {http://www.sciencedirect.com/science/article/pii/S0921889014000797}
    }

  • N. Abdo, L. Spinello, W. Burgard, and C. Stachniss, “Inferring What to Imitate in Manipulation Actions by Using a Recommender System,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Hong Kong, China, 2014.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Abdo2014,
    Title = {Inferring What to Imitate in Manipulation Actions by Using a Recommender System},
    Author = {N. Abdo and L. Spinello and W. Burgard and C. Stachniss},
    Booktitle = ICRA,
    Year = {2014},
    Address = {Hong Kong, China},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www2.informatik.uni-freiburg.de/~stachnis/pdf/abdo14icra.pdf}
    }

  • P. Agarwal, W. Burgard, and C. Stachniss, “Helmert’s and Bowie’s Geodetic Mapping Methods and Their Relation to Graph-Based SLAM,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Hong Kong, China, 2014.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Agarwal2014,
    Title = {Helmert's and Bowie's Geodetic Mapping Methods and Their Relation to Graph-Based SLAM},
    Author = {P. Agarwal and W. Burgard and C. Stachniss},
    Booktitle = ICRA,
    Year = {2014},
    Address = {Hong Kong, China},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.lifelong-navigation.eu/files/agarwal14bicra.pdf}
    }

  • P. Agarwal, W. Burgard, and C. Stachniss, “A Survey of Geodetic Approaches to Mapping and the Relationship to Graph-Based SLAM,” IEEE Robotics and Automation Magazine, vol. 21, pp. 63-80, 2014. doi:10.1109/MRA.2014.2322282
    [BibTeX] [PDF]
    The ability to simultaneously localize a robot and build a map of the environment is central to most robotics applications, and the problem is often referred to as simultaneous localization and mapping (SLAM). Robotics researchers have proposed a large variety of solutions allowing robots to build maps and use them for navigation. In addition, the geodetic community has addressed large-scale map building for centuries, computing maps that span across continents. These large-scale mapping processes had to deal with several challenges that are similar to those of the robotics community. In this article, we explain key geodetic map building methods that we believe are relevant for robot mapping. We also aim at providing a geodetic perspective on current state-of-the-art SLAM methods and identifying similarities both in terms of challenges faced and the solutions proposed by both communities. The central goal of this article is to connect both fields and enable future synergies between them.

    @Article{Agarwal2014b,
    Title = {A Survey of Geodetic Approaches to Mapping and the Relationship to Graph-Based SLAM},
    Author = {Pratik Agarwal and Wolfram Burgard and Cyrill Stachniss},
    Journal = {IEEE Robotics and Automation Magazine},
    Year = {2014},
    Pages = {63 - 80},
    Volume = {21},
    Abstract = {The ability to simultaneously localize a robot and build a map of the environment is central to most robotics applications, and the problem is often referred to as simultaneous localization and mapping (SLAM). Robotics researchers have proposed a large variety of solutions allowing robots to build maps and use them for navigation. In addition, the geodetic community has addressed large-scale map building for centuries, computing maps that span across continents. These large-scale mapping processes had to deal with several challenges that are similar to those of the robotics community. In this article, we explain key geodetic map building methods that we believe are relevant for robot mapping. We also aim at providing a geodetic perspective on current state-of-the-art SLAM methods and identifying similarities both in terms of challenges faced and the solutions proposed by both communities. The central goal of this article is to connect both fields and enable future synergies between them.},
    Doi = {10.1109/MRA.2014.2322282},
    Timestamp = {2014.09.18},
    Url = {http://www2.informatik.uni-freiburg.de/~stachnis/pdf/agarwal14ram-preprint.pdf}
    }

  • P. Agarwal, G. Grisetti, G. D. Tipaldi, L. Spinello, W. Burgard, and C. Stachniss, “Experimental Analysis of Dynamic Covariance Scaling for Robust Map Optimization Under Bad Initial Estimates,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Hong Kong, China, 2014.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Agarwal2014a,
    Title = {Experimental Analysis of Dynamic Covariance Scaling for Robust Map Optimization Under Bad Initial Estimates},
    Author = {P. Agarwal and G. Grisetti and G.D. Tipaldi and L. Spinello and W. Burgard and C. Stachniss},
    Booktitle = ICRA,
    Year = {2014},
    Address = {Hong Kong, China},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www2.informatik.uni-freiburg.de/~stachnis/pdf/agarwal14icra_dcs.pdf}
    }

  • S. Ito, F. Endres, M. Kuderer, G. D. Tipaldi, C. Stachniss, and W. Burgard, “W-RGB-D: Floor-Plan-Based Indoor Global Localization Using a Depth Camera and WiFi,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Hong Kong, China, 2014.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Ito2014,
    Title = {W-RGB-D: Floor-Plan-Based Indoor Global Localization Using a Depth Camera and WiFi},
    Author = {S. Ito and F. Endres and M. Kuderer and G.D. Tipaldi and C. Stachniss and W. Burgard},
    Booktitle = ICRA,
    Year = {2014},
    Address = {Hong Kong, China},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www2.informatik.uni-freiburg.de/~tipaldi/papers/ito14icra.pdf}
    }

  • R. Kümmerle, M. Ruhnke, B. Steder, C. Stachniss, and W. Burgard, “Autonomous Robot Navigation in Highly Populated Pedestrian Zones,” Journal of Field Robotics, 2014. doi:10.1002/rob.21534
    [BibTeX] [PDF]
    [none]
    @Article{kummerle14jfr,
    Title = {Autonomous Robot Navigation in Highly Populated Pedestrian Zones},
    Author = {K{\"u}mmerle, Rainer and Ruhnke, Michael and Steder, Bastian and Stachniss,Cyrill and Burgard, Wolfram},
    Journal = jfr,
    Year = {2014},
    Abstract = {[none]},
    Doi = {10.1002/rob.21534},
    Timestamp = {2015.01.22},
    Url = {http://ais.informatik.uni-freiburg.de/publications/papers/kuemmerle14jfr.pdf}
    }

  • M. Mazuran, G. D. Tipaldi, L. Spinello, W. Burgard, and C. Stachniss, “A Statistical Measure for Map Consistency in SLAM,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Hong Kong, China, 2014.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Mazuran2014,
    Title = {A Statistical Measure for Map Consistency in SLAM},
    Author = {M. Mazuran and G.D. Tipaldi and L. Spinello and W. Burgard and C. Stachniss},
    Booktitle = ICRA,
    Year = {2014},
    Address = {Hong Kong, China},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www2.informatik.uni-freiburg.de/~stachnis/pdf/mazuran14icra.pdf}
    }

  • T. Naseer, L. Spinello, W. Burgard, and Stachniss, “Robust Visual Robot Localization Across Seasons using Network Flows,” in Proceedings of the National Conference on Artificial Intelligence (AAAI) , 2014.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Naseer2014,
    Title = {Robust Visual Robot Localization Across Seasons using Network Flows},
    Author = {Naseer, T. and Spinello, L. and Burgard, W. and Stachniss},
    Booktitle = aaai,
    Year = {2014},
    Abstract = {[none]},
    Timestamp = {2014.05.12},
    Url = {http://www2.informatik.uni-freiburg.de/~stachnis/pdf/naseer14aaai.pdf}
    }

  • F. Nenci, L. Spinello, and C. Stachniss, “Effective Compression of Range Data Streams for Remote Robot Operations using H.264,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2014.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Nenci2014,
    Title = {Effective Compression of Range Data Streams for Remote Robot Operations using H.264},
    Author = {Fabrizio Nenci and Luciano Spinello and Cyrill Stachniss},
    Booktitle = iros,
    Year = {2014},
    Abstract = {[none]},
    Timestamp = {2014.09.18},
    Url = {http://www2.informatik.uni-freiburg.de/~stachnis/pdf/nenci14iros.pdf}
    }

  • S. Oßwald, H. Kretzschmar, W. Burgard, and C. Stachniss, “Learning to Give Route Directions from Human Demonstrations,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Hong Kong, China, 2014.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Osswald2014,
    Title = {Learning to Give Route Directions from Human Demonstrations},
    Author = {S. O{\ss}wald and H. Kretzschmar and W. Burgard and C. Stachniss},
    Booktitle = ICRA,
    Year = {2014},
    Address = {Hong Kong, China},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www2.informatik.uni-freiburg.de/~kretzsch/pdf/osswald14icra.pdf}
    }

  • C. Stachniss and W. Burgard, “Particle Filters for Robot Navigation,” Foundations and Trends in Robotics, vol. 3, iss. 4, pp. 211-282, 2014. doi:10.1561/2300000013
    [BibTeX] [PDF]
    [none]
    @Article{Stachniss2014,
    Title = {Particle Filters for Robot Navigation},
    Author = {C. Stachniss and W. Burgard},
    Journal = fntr,
    Year = {2014},
    Month = {2012, published 2014},
    Number = {4},
    Pages = {211-282},
    Volume = {3},
    Abstract = {[none]},
    Doi = {10.1561/2300000013},
    Timestamp = {2014.04.24},
    Url = {http://www.nowpublishers.com/articles/foundations-and-trends-in-robotics/ROB-013}
    }

  • O. Vysotska, B. Frank, I. Ulbert, O. Paul, P. Ruther, C. Stachniss, and W. Burgard, “Automatic Channel Selection and Neural Signal Estimation across Channels of Neural Probes,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Chicago, USA, 2014.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Vysotska2014,
    Title = {Automatic Channel Selection and Neural Signal Estimation across Channels of Neural Probes},
    Author = {O. Vysotska and B. Frank and I. Ulbert and O. Paul and P. Ruther and C. Stachniss and W. Burgard},
    Booktitle = iros,
    Year = {2014},
    Address = {Chicago, USA},
    Abstract = {[none]},
    Timestamp = {2014.09.22},
    Url = {http://www2.informatik.uni-freiburg.de/~stachnis/pdf/vysotska14iros.pdf}
    }

  • V. A. Ziparo, G. Castelli, L. Van Gool, G. Grisetti, B. Leibe, M. Proesmans, and C. Stachniss, “The ROVINA Project. Robots for Exploration, Digital Preservation and Visualization of Archeological sites,” in Proc. of the 18th ICOMOS General Assembly and Scientific Symposium “Heritage and Landscape as Human Values" , 2014.
    [BibTeX]
    [none]
    @InProceedings{ziparo14icomosga,
    Title = {The ROVINA Project. Robots for Exploration, Digital Preservation and Visualization of Archeological sites},
    Author = {Ziparo, V.A. and Castelli, G. and Van Gool, L. and Grisetti, G. and Leibe, B. and Proesmans, M. and Stachniss, C.},
    Booktitle = {Proc. of the 18th ICOMOS General Assembly and Scientific Symposium ``Heritage and Landscape as Human Values"},
    Year = {2014},
    Abstract = {[none]},
    Timestamp = {2015.03.02}
    }

2013

  • N. Abdo, H. Kretzschmar, L. Spinello, and C. Stachniss, “Learning Manipulation Actions from a Few Demonstrations,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Karlsruhe, Germany, 2013.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Abdo2013,
    Title = {Learning Manipulation Actions from a Few Demonstrations},
    Author = {N. Abdo and H. Kretzschmar and L. Spinello and C. Stachniss},
    Booktitle = ICRA,
    Year = {2013},
    Address = {Karlsruhe, Germany},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/abdo13icra.pdf}
    }

  • P. Agarwal, G. D. Tipaldi, L. Spinello, C. Stachniss, and W. Burgard, “Dynamic Covariance Scaling for Robust Robotic Mapping,” in ICRA Workshop on robust and Multimodal Inference in Factor Graphs , Karlsruhe, Germany, 2013.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Agarwal2013,
    Title = {Dynamic Covariance Scaling for Robust Robotic Mapping},
    Author = {P. Agarwal and G.D. Tipaldi and L. Spinello and C. Stachniss and W. Burgard},
    Booktitle = {ICRA Workshop on robust and Multimodal Inference in Factor Graphs},
    Year = {2013},
    Address = {Karlsruhe, Germany},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/agarwal13icraws.pdf}
    }

  • P. Agarwal, G. D. Tipaldi, L. Spinello, C. Stachniss, and W. Burgard, “Robust Map Optimization using Dynamic Covariance Scaling,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Karlsruhe, Germany, 2013.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Agarwal2013a,
    Title = {Robust Map Optimization using Dynamic Covariance Scaling},
    Author = {P. Agarwal and G.D. Tipaldi and L. Spinello and C. Stachniss and W. Burgard},
    Booktitle = ICRA,
    Year = {2013},
    Address = {Karlsruhe, Germany},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/agarwal13icra.pdf}
    }

  • I. Bogoslavskyi, O. Vysotska, J. Serafin, G. Grisetti, and C. Stachniss, “Efficient Traversability Analysis for Mobile Robots using the Kinect Sensor,” in Proceedings of the European Conference on Mobile Robots (ECMR) , Barcelona, Spain, 2013.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Bogoslavskyi2013,
    Title = {Efficient Traversability Analysis for Mobile Robots using the Kinect Sensor},
    Author = {I. Bogoslavskyi and O. Vysotska and J. Serafin and G. Grisetti and C. Stachniss},
    Booktitle = ECMR,
    Year = {2013},
    Address = {Barcelona, Spain},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/bogoslavskyi13ecmr.pdf}
    }

  • W. Burgard and C. Stachniss, “Gestatten, Obelix!,” Forschung — Das Magazin der Deutschen Forschungsgemeinschaft, vol. 1, 2013.
    [BibTeX] [PDF]
    [none]
    @Article{Burgard2013,
    Title = {Gestatten, Obelix!},
    Author = {W. Burgard and C. Stachniss},
    Journal = {Forschung -- Das Magazin der Deutschen Forschungsgemeinschaft},
    Year = {2013},
    Note = {In German, invited},
    Volume = {1},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/forschung_2013_01-pg4-9.pdf}
    }

  • A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “OctoMap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees,” Autonomous Robots, vol. 34, pp. 189-206, 2013.
    [BibTeX] [PDF]
    [none]
    @Article{Hornung2013,
    Title = {{OctoMap}: An Efficient Probabilistic 3D Mapping Framework Based on Octrees},
    Author = {A. Hornung and K.M. Wurm and M. Bennewitz and C. Stachniss and W. Burgard},
    Journal = auro,
    Year = {2013},
    Pages = {189-206},
    Volume = {34},
    Abstract = {[none]},
    Issue = {3},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/hornung13auro.pdf}
    }

  • R. Kümmerle, M. Ruhnke, B. Steder, C. Stachniss, and W. Burgard, “A Navigation System for Robots Operating in Crowded Urban Environments,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Karlsruhe, Germany, 2013.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Kummerle2013,
    Title = {A Navigation System for Robots Operating in Crowded Urban Environments},
    Author = {R. K\"ummerle and M. Ruhnke and B. Steder and C. Stachniss and W. Burgard},
    Booktitle = ICRA,
    Year = {2013},
    Address = {Karlsruhe, Germany},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/kuemmerle13icra.pdf}
    }

  • D. Maier, C. Stachniss, and M. Bennewitz, “Vision-Based Humanoid Navigation Using Self-Supervised Obstacle Detection,” The Int. Journal of Humanoid Robotics (IJHR), vol. 10, 2013.
    [BibTeX] [PDF]
    [none]
    @Article{Maier2013,
    Title = {Vision-Based Humanoid Navigation Using Self-Supervised Obstacle Detection},
    Author = {D. Maier and C. Stachniss and M. Bennewitz},
    Journal = ijhr,
    Year = {2013},
    Volume = {10},
    Abstract = {[none]},
    Issue = {2},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/maier13ijhr.pdf}
    }

  • K. M. Wurm, C. Dornhege, B. Nebel, W. Burgard, and C. Stachniss, “Coordinating Heterogeneous Teams of Robots using Temporal Symbolic Planning,” Autonomous Robots, vol. 34, 2013.
    [BibTeX] [PDF]
    [none]
    @Article{Wurm2013,
    Title = {Coordinating Heterogeneous Teams of Robots using Temporal Symbolic Planning},
    Author = {K.M. Wurm and C. Dornhege and B. Nebel and W. Burgard and C. Stachniss},
    Journal = auro,
    Year = {2013},
    Volume = {34},
    Abstract = {[none]},
    Issue = {4},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/wurm13auro.pdf}
    }

  • K. M. Wurm, H. Kretzschmar, R. Kümmerle, C. Stachniss, and W. Burgard, “Identifying Vegetation from Laser Data in Structured Outdoor Environments,” Robotics and Autonomous Systems, 2013.
    [BibTeX] [PDF]
    [none]
    @Article{Wurm2013a,
    Title = {Identifying Vegetation from Laser Data in Structured Outdoor Environments},
    Author = {K.M. Wurm and H. Kretzschmar and R. K{\"u}mmerle and C. Stachniss and W. Burgard},
    Journal = jras,
    Year = {2013},
    Note = {In press},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/wurm13ras.pdf}
    }

2012

  • N. Abdo, H. Kretzschmar, and C. Stachniss, “From Low-Level Trajectory Demonstrations to Symbolic Actions for Planning,” in Proceedings of the ICAPS Workshop on Combining Task and Motion Planning for Real-World Applications (TAMPRA) , 2012.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Abdo2012,
    Title = {From Low-Level Trajectory Demonstrations to Symbolic Actions for Planning},
    Author = {N. Abdo and H. Kretzschmar and C. Stachniss},
    Booktitle = {Proceedings of the ICAPS Workshop on Combining Task and Motion Planning for Real-World Applications (TAMPRA)},
    Year = {2012},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/abdo12tampra.pdf}
    }

  • G. Grisetti, L. Iocchi, B. Leibe, V. A. Ziparo, and C. Stachniss, “Digitization of Inaccessible Archeological Sites with Autonomous Mobile Robots,” in Conference on Robotics Innovation for Cultural Heritage , 2012.
    [BibTeX]
    [none]
    @InProceedings{Grisetti2012,
    Title = {Digitization of Inaccessible Archeological Sites with Autonomous Mobile Robots},
    Author = {G. Grisetti and L. Iocchi and B. Leibe and V.A. Ziparo and C. Stachniss},
    Booktitle = {Conference on Robotics Innovation for Cultural Heritage},
    Year = {2012},
    Abstract = {[none]},
    Notes = {Extended abstract},
    Timestamp = {2014.04.24}
    }

  • D. Joho, G. D. Tipaldi, N. Engelhard, C. Stachniss, and W. Burgard, “Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction,” in Proceedings of Robotics: Science and Systems (RSS) , 2012.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Joho2012,
    Title = {Nonparametric {B}ayesian Models for Unsupervised Scene Analysis and Reconstruction},
    Author = {D. Joho and G.D. Tipaldi and N. Engelhard and C. Stachniss and W. Burgard},
    Booktitle = rss,
    Year = {2012},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/joho12rss.pdf}
    }

  • H. Kretzschmar and C. Stachniss, “Information-Theoretic Pose Graph Compression for Laser-based SLAM,” The International Journal of Robotics Research, vol. 31, pp. 1219-1230, 2012.
    [BibTeX] [PDF]
    [none]
    @Article{Kretzschmar2012,
    Title = {Information-Theoretic Pose Graph Compression for Laser-based {SLAM}},
    Author = {H. Kretzschmar and C. Stachniss},
    Journal = ijrr,
    Year = {2012},
    Pages = {1219--1230},
    Volume = {31},
    Abstract = {[none]},
    Issue = {11},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/kretzschmar12ijrr.pdf}
    }

  • J. Roewekaemper, C. Sprunk, G. D. Tipaldi, C. Stachniss, P. Pfaff, and W. Burgard, “On the Position Accuracy of Mobile Robot Localization based on Particle Filters combined with Scan Matching,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2012.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Roewekaemper2012,
    Title = {On the Position Accuracy of Mobile Robot Localization based on Particle Filters combined with Scan Matching},
    Author = {J. Roewekaemper and C. Sprunk and G.D. Tipaldi and C. Stachniss and P. Pfaff and W. Burgard},
    Booktitle = IROS,
    Year = {2012},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://ais.informatik.uni-freiburg.de/publications/papers/roewekaemper12iros.pdf}
    }

  • L. Spinello, C. Stachniss, and W. Burgard, “Scene in the Loop: Towards Adaptation-by-Tracking in RGB-D Data,” in Proceedings of the RSS Workshop RGB-D: Advanced Reasoning with Depth Cameras , 2012.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Spinello2012,
    Title = {Scene in the Loop: Towards Adaptation-by-Tracking in RGB-D Data},
    Author = {L. Spinello and C. Stachniss and W. Burgard},
    Booktitle = {Proceedings of the RSS Workshop RGB-D: Advanced Reasoning with Depth Cameras},
    Year = {2012},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/spinello12rssws.pdf}
    }

2011

  • S. Asadi, M. Reggente, C. Stachniss, C. Plagemann, and A. J. Lilienthal, “Intelligent Systems for Machine Olfaction: Tools and Methodologies,” , E. L. Hines and M. S. Leeson, Eds., {IGI} {G}lobal, 2011, pp. 153-179.
    [BibTeX]
    [none]
    @InBook{Asadi2011,
    Title = {Intelligent Systems for Machine Olfaction: Tools and Methodologies},
    Author = {S. Asadi and M. Reggente and C. Stachniss and C. Plagemann and A.J. Lilienthal},
    Chapter = {Statistical Gas Distribution Modelling using Kernel Methods},
    Editor = {E.L. Hines and M.S. Leeson},
    Pages = {153-179},
    Publisher = {{IGI} {G}lobal},
    Year = {2011},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • J. Becker, C. Bersch, D. Pangercic, B. Pitzer, T. Rühr, B. Sankaran, J. Sturm, C. Stachniss, M. Beetz, and W. Burgard, “Mobile Manipulation of Kitchen Containers,” in Proceedings of the IROS’11 Workshop on Results, Challenges and Lessons Learned in Advancing Robots with a Common Platform , San Francisco, CA, USA, 2011.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Becker2011,
    Title = {Mobile Manipulation of Kitchen Containers},
    Author = {J. Becker and C. Bersch and D. Pangercic and B. Pitzer and T. R\"uhr and B. Sankaran and J. Sturm and C. Stachniss and M. Beetz and W. Burgard},
    Booktitle = {Proceedings of the IROS'11 Workshop on Results, Challenges and Lessons Learned in Advancing Robots with a Common Platform},
    Year = {2011},
    Address = {San Francisco, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/becker11irosws.pdf}
    }

  • M. Bennewitz, D. Maier, A. Hornung, and C. Stachniss, “Integrated Perception and Navigation in Complex Indoor Environments,” in Proceedings of the IEEE-RAS Int. Conf. on Humanoid Robots (HUMANOIDS) , 2011.
    [BibTeX]
    [none]
    @InProceedings{Bennewitz2011,
    Title = {Integrated Perception and Navigation in Complex Indoor Environments},
    Author = {M. Bennewitz and D. Maier and A. Hornung and C. Stachniss},
    Booktitle = {Proceedings of the IEEE-RAS Int. Conf. on Humanoid Robots (HUMANOIDS)},
    Year = {2011},
    Note = {Invited presentation at the workshop on Humanoid service robot navigation in crowded and dynamic environments},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • B. Frank, C. Stachniss, N. Abdo, and W. Burgard, “Using Gaussian Process Regression for Efficient Motion Planning in Environments with Deformable Objects,” in Proc. of the AAAI-11 Workshop on Automated Action Planning for Autonomous Mobile Robots (PAMR) , San Francisco, CA, USA, 2011.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Frank2011,
    Title = {Using Gaussian Process Regression for Efficient Motion Planning in Environments with Deformable Objects},
    Author = {B. Frank and C. Stachniss and N. Abdo and W. Burgard},
    Booktitle = {Proc. of the AAAI-11 Workshop on Automated Action Planning for Autonomous Mobile Robots (PAMR)},
    Year = {2011},
    Address = {San Francisco, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/frank11pamr.pdf}
    }

  • B. Frank, C. Stachniss, N. Abdo, and W. Burgard, “Efficient Motion Planning for Manipulation Robots in Environments with Deformable Objects,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , San Francisco, CA, USA, 2011.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Frank2011a,
    Title = {Efficient Motion Planning for Manipulation Robots in Environments with Deformable Objects},
    Author = {B. Frank and C. Stachniss and N. Abdo and W. Burgard},
    Booktitle = IROS,
    Year = {2011},
    Address = {San Francisco, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/frank11iros.pdf}
    }

  • R. Kümmerle, G. Grisetti, C. Stachniss, and W. Burgard, “Simultaneous Parameter Calibration, Localization, and Mapping for Robust Service Robotics,” in Proceedings of the IEEE Workshop on Advanced Robotics and its Social Impacts , Half-Moon Bay, CA, USA, 2011.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Kummerle2011,
    Title = {Simultaneous Parameter Calibration, Localization, and Mapping for Robust Service Robotics},
    Author = {R. K\"ummerle and G. Grisetti and C. Stachniss and W. Burgard},
    Booktitle = {Proceedings of the IEEE Workshop on Advanced Robotics and its Social Impacts},
    Year = {2011},
    Address = {Half-Moon Bay, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/kuemmerle11arso.pdf}
    }

  • H. Kretzschmar and C. Stachniss, “Pose Graph Compression for Laser-based SLAM,” in Proceedings of the Int. Symposium of Robotics Research (ISRR) , Flagstaff, AZ, USA, 2011.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Kretzschmar2011a,
    Title = {Pose Graph Compression for Laser-based {SLAM}},
    Author = {H. Kretzschmar and C. Stachniss},
    Booktitle = ISRR,
    Year = {2011},
    Address = {Flagstaff, AZ, USA},
    Note = {Invited presentation},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss11isrr.pdf}
    }

  • H. Kretzschmar, C. Stachniss, and G. Grisetti, “Efficient Information-Theoretic Graph Pruning for Graph-Based SLAM with Laser Range Finders,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , San Francisco, CA, USA, 2011.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Kretzschmar2011,
    Title = {Efficient Information-Theoretic Graph Pruning for Graph-Based {SLAM} with Laser Range Finders},
    Author = {H. Kretzschmar and C. Stachniss and G. Grisetti},
    Booktitle = IROS,
    Year = {2011},
    Address = {San Francisco, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/kretzschmar11iros.pdf}
    }

  • D. Maier, M. Bennewitz, and C. Stachniss, “Self-supervised Obstacle Detection for Humanoid Navigation Using Monocular Vision and Sparse Laser Data,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Shanghai, China, 2011.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Maier2011,
    Title = {Self-supervised Obstacle Detection for Humanoid Navigation Using Monocular Vision and Sparse Laser Data},
    Author = {D. Maier and M. Bennewitz and C. Stachniss},
    Booktitle = ICRA,
    Year = {2011},
    Address = {Shanghai, China},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/maier11icra.pdf}
    }

  • J. Sturm, C. Stachniss, and W. Burgard, “A Probabilistic Framework for Learning Kinematic Models of Articulated Objects,” Journal on Artificial Intelligence Research, vol. 41, pp. 477-526, 2011.
    [BibTeX] [PDF]
    [none]
    @Article{Sturm2011,
    Title = {A Probabilistic Framework for Learning Kinematic Models of Articulated Objects},
    Author = {J. Sturm and C. Stachniss and W. Burgard},
    Journal = jair,
    Year = {2011},
    Pages = {477--526},
    Volume = {41},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/sturm11jair.pdf}
    }

  • K. M. Wurm, D. Hennes, D. Holz, R. B. Rusu, C. Stachniss, K. Konolige, and W. Burgard, “Hierarchies of Octrees for Efficient 3D Mapping,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , San Francisco, CA, USA, 2011.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Wurm2011,
    Title = {Hierarchies of Octrees for Efficient 3D Mapping},
    Author = {K.M. Wurm and D. Hennes and D. Holz and R.B. Rusu and C. Stachniss and K. Konolige and W. Burgard},
    Booktitle = IROS,
    Year = {2011},
    Address = {San Francisco, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/wurm11iros.pdf}
    }

  • J. Ziegler, H. Kretzschmar, C. Stachniss, G. Grisetti, and W. Burgard, “Accurate Human Motion Capture in Large Areas by Combining IMU- and Laser-based People Tracking,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , San Francisco, CA, USA, 2011.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Ziegler2011,
    Title = {Accurate Human Motion Capture in Large Areas by Combining IMU- and Laser-based People Tracking},
    Author = {J. Ziegler and H. Kretzschmar and C. Stachniss and G. Grisetti and W. Burgard},
    Booktitle = IROS,
    Year = {2011},
    Address = {San Francisco, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/ziegler11iros.pdf}
    }

2010

  • W. Burgard, K. M. Wurm, M. Bennewitz, C. Stachniss, A. Hornung, R. B. Rusu, and K. Konolige, “Modeling the World Around Us: An Efficient 3D Representation for Personal Robotics,” in Workshop on Defining and Solving Realistic Perception Problems in Personal Robotics at the IEEE/RSJ Int.Conf.on Intelligent Robots and Systems , Taipei, Taiwan, 2010.
    [BibTeX]
    [none]
    @InProceedings{Burgard2010,
    Title = {Modeling the World Around Us: An Efficient 3D Representation for Personal Robotics},
    Author = {Burgard, W. and Wurm, K.M. and Bennewitz, M. and Stachniss, C. and Hornung, A. and Rusu, R.B. and Konolige, K.},
    Booktitle = {Workshop on Defining and Solving Realistic Perception Problems in Personal Robotics at the IEEE/RSJ Int.Conf.on Intelligent Robots and Systems},
    Year = {2010},
    Address = {Taipei, Taiwan},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • B. Frank, R. Schmedding, C. Stachniss, M. Teschner, and W. Burgard, “Learning Deformable Object Models for Mobile Robot Path Planning using Depth Cameras and a Manipulation Robot,” in Proceedings of the Workshop RGB-D: Advanced Reasoning with Depth Cameras at Robotics: Science and Systems (RSS) , Zaragoza, Spain, 2010.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Frank2010,
    Title = {Learning Deformable Object Models for Mobile Robot Path Planning using Depth Cameras and a Manipulation Robot},
    Author = {B. Frank and R. Schmedding and C. Stachniss and M. Teschner and W. Burgard},
    Booktitle = {Proceedings of the Workshop RGB-D: Advanced Reasoning with Depth Cameras at Robotics: Science and Systems (RSS)},
    Year = {2010},
    Address = {Zaragoza, Spain},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/frank10rssws.pdf}
    }

  • B. Frank, R. Schmedding, C. Stachniss, M. Teschner, and W. Burgard, “Learning the Elasticity Parameters of Deformable Objects with a Manipulation Robot,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Taipei, Taiwan, 2010.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Frank2010a,
    Title = {Learning the Elasticity Parameters of Deformable Objects with a Manipulation Robot},
    Author = {B. Frank and R. Schmedding and C. Stachniss and M. Teschner and W. Burgard},
    Booktitle = iros,
    Year = {2010},
    Address = {Taipei, Taiwan},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/frank10iros.pdf}
    }

  • G. Grisetti, R. Kümmerle, C. Stachniss, and W. Burgard, “A Tutorial on Graph-based SLAM,” IEEE Transactions on Intelligent Transportation Systems Magazine, vol. 2, pp. 31-43, 2010.
    [BibTeX] [PDF]
    [none]
    @Article{Grisetti2010a,
    Title = {A Tutorial on Graph-based {SLAM}},
    Author = {G. Grisetti and R. K{\"u}mmerle and C. Stachniss and W. Burgard},
    Journal = {IEEE Transactions on Intelligent Transportation Systems Magazine},
    Year = {2010},
    Pages = {31--43},
    Volume = {2},
    Abstract = {[none]},
    Issue = {4},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf}
    }

  • G. Grisetti, R. Kümmerle, C. Stachniss, U. Frese, and C. Hertzberg, “Hierarchical Optimization on Manifolds for Online 2D and 3D Mapping,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Anchorage, Alaska, 2010.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Grisetti2010,
    Title = {Hierarchical Optimization on Manifolds for Online 2D and 3D Mapping},
    Author = {G. Grisetti and R. K{\"u}mmerle and C. Stachniss and U. Frese and C. Hertzberg},
    Booktitle = icra,
    Year = {2010},
    Address = {Anchorage, Alaska},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10icra.pdf}
    }

  • A. Hornung, M.Bennewitz, C. Stachniss, H. Strasdat, S. Oßwald, and W. Burgard, “Learning Adaptive Navigation Strategies for Resource-Constrained Systems,” in Proceedings of the Int. Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems , Lisbon, Portugal, 2010.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Hornung2010,
    Title = {Learning Adaptive Navigation Strategies for Resource-Constrained Systems},
    Author = {A. Hornung and M.Bennewitz and C. Stachniss and H. Strasdat and S. O{\ss}wald and W. Burgard},
    Booktitle = {Proceedings of the Int. Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems},
    Year = {2010},
    Address = {Lisbon, Portugal},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/hornung10erlars.pdf}
    }

  • M. Karg, K. M. Wurm, C. Stachniss, K. Dietmayer, and W. Burgard, “Consistent Mapping of Multistory Buildings by Introducing Global Constraints to Graph-based SLAM,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Anchorage, Alaska, 2010.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Karg2010,
    Title = {Consistent Mapping of Multistory Buildings by Introducing Global Constraints to Graph-based {SLAM}},
    Author = {M. Karg and K.M. Wurm and C. Stachniss and K. Dietmayer and W. Burgard},
    Booktitle = icra,
    Year = {2010},
    Address = {Anchorage, Alaska},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/karg10icra.pdf}
    }

  • H. Kretzschmar, G. Grisetti, and C. Stachniss, “Lifelong Map Learning for Graph-based SLAM in Static Environments,” KI — Künstliche Intelligenz, vol. 24, pp. 199-206, 2010.
    [BibTeX]
    [none]
    @Article{Kretzschmar2010,
    Title = {Lifelong Map Learning for Graph-based {SLAM} in Static Environments},
    Author = {H. Kretzschmar and G. Grisetti and C. Stachniss},
    Journal = {{KI} -- {K}\"unstliche {I}ntelligenz},
    Year = {2010},
    Pages = {199--206},
    Volume = {24},
    Abstract = {[none]},
    Issue = {3},
    Timestamp = {2014.04.24}
    }

  • J. Müller, C. Stachniss, K. O. Arras, and W. Burgard, “Socially Inspired Motion Planning for Mobile Robots in Populated Environments,” in Cognitive Systems, Springer, 2010.
    [BibTeX]
    [none]
    @InCollection{Muller2010,
    Title = {Socially Inspired Motion Planning for Mobile Robots in Populated Environments},
    Author = {M\"{u}ller, J. and Stachniss, C. and Arras, K.O. and Burgard, W.},
    Booktitle = {Cognitive Systems},
    Publisher = springer,
    Year = {2010},
    Note = {In press},
    Series = {Cognitive Systems Monographs},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • C. Plagemann, C. Stachniss, J. Hess, F. Endres, and N. Franklin, “A Nonparametric Learning Approach to Range Sensing from Omnidirectional Vision,” Robotics and Autonomous Systems, vol. 58, pp. 762-772, 2010.
    [BibTeX]
    [none]
    @Article{Plagemann2010,
    Title = {A Nonparametric Learning Approach to Range Sensing from Omnidirectional Vision},
    Author = {C. Plagemann and C. Stachniss and J. Hess and F. Endres and N. Franklin},
    Journal = jras,
    Year = {2010},
    Pages = {762--772},
    Volume = {58},
    Abstract = {[none]},
    Issue = {6},
    Timestamp = {2014.04.24}
    }

  • J. Sturm, A. Jain, C. Stachniss, C. C. Kemp, and W. Burgard, “Robustly Operating Articulated Objects based on Experience,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Taipei, Taiwan, 2010.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Sturm2010b,
    Title = {Robustly Operating Articulated Objects based on Experience},
    Author = {J. Sturm and A. Jain and C. Stachniss and C.C. Kemp and W. Burgard},
    Booktitle = iros,
    Year = {2010},
    Address = {Taipei, Taiwan},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/sturm10iros.pdf}
    }

  • J. Sturm, K. Konolige, C. Stachniss, and W. Burgard, “Vision-based Detection for Learning Articulation Models of Cabinet Doors and Drawers in Household Environments,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Anchorage, Alaska, 2010.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Sturm2010,
    Title = {Vision-based Detection for Learning Articulation Models of Cabinet Doors and Drawers in Household Environments},
    Author = {J. Sturm and K. Konolige and C. Stachniss and W. Burgard},
    Booktitle = icra,
    Year = {2010},
    Address = {Anchorage, Alaska},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/sturm10icra.pdf}
    }

  • J. Sturm, K. Konolige, C. Stachniss, and W. Burgard, “3D Pose Estimation, Tracking and Model Learning of Articulated Objects from Dense Depth Video using Projected Texture Stereo,” in Proceedings of the Workshop RGB-D: Advanced Reasoning with Depth Cameras at Robotics: Science and Systems (RSS) , Zaragoza, Spain, 2010.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Sturm2010a,
    Title = {3D Pose Estimation, Tracking and Model Learning of Articulated Objects from Dense Depth Video using Projected Texture Stereo},
    Author = {J. Sturm and K. Konolige and C. Stachniss and W. Burgard},
    Booktitle = {Proceedings of the Workshop RGB-D: Advanced Reasoning with Depth Cameras at Robotics: Science and Systems (RSS)},
    Year = {2010},
    Address = {Zaragoza, Spain},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/sturm10rssws.pdf}
    }

  • K. M. Wurm, C. Dornhege, P. Eyerich, C. Stachniss, B. Nebel, and W. Burgard, “Coordinated Exploration with Marsupial Teams of Robots using Temporal Symbolic Planning,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Taipei, Taiwan, 2010.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Wurm2010a,
    Title = {Coordinated Exploration with Marsupial Teams of Robots using Temporal Symbolic Planning},
    Author = {K.M. Wurm and C. Dornhege and P. Eyerich and C. Stachniss and B. Nebel and W. Burgard},
    Booktitle = iros,
    Year = {2010},
    Address = {Taipei, Taiwan},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/wurm10iros.pdf}
    }

  • K. M. Wurm, A. Hornung, M. Bennewitz, C. Stachniss, and W. Burgard, “OctoMap: A Probabilistic, Flexible, and Compact 3D Map Representation for Robotic Systems,” in Proc. of the ICRA 2010 Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation , Anchorage, AK, USA, 2010.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Wurm2010,
    Title = {{OctoMap}: A Probabilistic, Flexible, and Compact {3D} Map Representation for Robotic Systems},
    Author = {K.M. Wurm and A. Hornung and M. Bennewitz and C. Stachniss and W. Burgard},
    Booktitle = {Proc. of the ICRA 2010 Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation},
    Year = {2010},
    Address = {Anchorage, AK, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/wurm10icraws.pdf}
    }

  • K. M. Wurm, C. Stachniss, and G. Grisetti, “Bridging the Gap Between Feature- and Grid-based SLAM,” Robotics and Autonomous Systems, vol. 58, iss. 2, pp. 140-148, 2010. doi:10.1016/j.robot.2009.09.009
    [BibTeX] [PDF]
    [none]
    @Article{Wurm2010b,
    Title = {Bridging the Gap Between Feature- and Grid-based SLAM},
    Author = {Wurm, K.M. and Stachniss, C. and Grisetti, G.},
    Journal = jras,
    Year = {2010},
    Number = {2},
    Pages = {140 - 148},
    Volume = {58},
    Abstract = {[none]},
    Doi = {10.1016/j.robot.2009.09.009},
    ISSN = {0921-8890},
    Timestamp = {2014.04.24},
    Url = {http://ais.informatik.uni-freiburg.de/publications/papers/wurm10ras.pdf}
    }

2009

  • M. Bennewitz, C. Stachniss, S. Behnke, and W. Burgard, “Utilizing Reflection Properties of Surfaces to Improve Mobile Robot Localization,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Kobe, Japan, 2009.
    [BibTeX]
    [none]
    @InProceedings{Bennewitz2009,
    Title = {Utilizing Reflection Properties of Surfaces to Improve Mobile Robot Localization},
    Author = {M. Bennewitz and Stachniss, C. and Behnke, S. and Burgard, W.},
    Booktitle = icra,
    Year = {2009},
    Address = {Kobe, Japan},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • W. Burgard, C. Stachniss, G. Grisetti, B. Steder, R. Kümmerle, C. Dornhege, M. Ruhnke, A. Kleiner, and J. D. Tardós, “A Comparison of SLAM Algorithms Based on a Graph of Relations,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2009.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Burgard2009,
    Title = {A Comparison of {SLAM} Algorithms Based on a Graph of Relations},
    Author = {W. Burgard and C. Stachniss and G. Grisetti and B. Steder and R. K\"ummerle and C. Dornhege and M. Ruhnke and A. Kleiner and J.D. Tard\'os},
    Booktitle = iros,
    Year = {2009},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/burgard09iros.pdf}
    }

  • F. Endres, J. Hess, N. Franklin, C. Plagemann, C. Stachniss, and W. Burgard, “Estimating Range Information from Monocular Vision,” in Workshop Regression in Robotics – Approaches and Applications at Robotics: Science and Systems (RSS) , Seattle, WA, USA, 2009.
    [BibTeX]
    [none]
    @InProceedings{Endres2009,
    Title = {Estimating Range Information from Monocular Vision},
    Author = {Endres, F. and Hess, J. and Franklin, N. and Plagemann, C. and Stachniss, C. and Burgard, W.},
    Booktitle = {Workshop Regression in Robotics - Approaches and Applications at Robotics: Science and Systems (RSS)},
    Year = {2009},
    Address = {Seattle, WA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • F. Endres, C. Plagemann, C. Stachniss, and W. Burgard, “Scene Analysis using Latent Dirichlet Allocation,” in Proceedings of Robotics: Science and Systems (RSS) , Seattle, WA, USA, 2009.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Endres2009a,
    Title = {Scene Analysis using Latent Dirichlet Allocation},
    Author = {F. Endres and C. Plagemann and Stachniss, C. and Burgard, W.},
    Booktitle = RSS,
    Year = {2009},
    Address = {Seattle, WA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/endres09rss-draft.pdf}
    }

  • C. Eppner, J. Sturm, M. Bennewitz, C. Stachniss, and W. Burgard, “Imitation Learning with Generalized Task Descriptions,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Kobe, Japan, 2009.
    [BibTeX]
    [none]
    @InProceedings{Eppner2009,
    Title = {Imitation Learning with Generalized Task Descriptions},
    Author = {C. Eppner and J. Sturm and M. Bennewitz and Stachniss, C. and Burgard, W.},
    Booktitle = icra,
    Year = {2009},
    Address = {Kobe, Japan},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • B. Frank, C. Stachniss, R. Schmedding, W. Burgard, and M. Teschner, “Real-world Robot Navigation amongst Deformable Obstacles,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Kobe, Japan, 2009.
    [BibTeX]
    [none]
    @InProceedings{Frank2009,
    Title = {Real-world Robot Navigation amongst Deformable Obstacles},
    Author = {B. Frank and C. Stachniss and R. Schmedding and W. Burgard and M. Teschner},
    Booktitle = icra,
    Year = {2009},
    Address = {Kobe, Japan},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • G. Grisetti, C. Stachniss, and W. Burgard, “Non-linear Constraint Network Optimization for Efficient Map Learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 10, iss. 3, pp. 428-439, 2009.
    [BibTeX] [PDF]
    [none]
    @Article{Grisetti2009,
    Title = {Non-linear Constraint Network Optimization for Efficient Map Learning},
    Author = {Grisetti, G. and Stachniss, C. and Burgard, W.},
    Journal = ieeeits,
    Year = {2009},
    Number = {3},
    Pages = {428--439},
    Volume = {10},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/grisetti09its.pdf}
    }

  • R. Kuemmerle, B. Steder, C. Dornhege, M. Ruhnke, G. Grisetti, C. Stachniss, and A. Kleiner, “On measuring the accuracy of SLAM algorithms,” Autonomous Robots, vol. 27, p. 387ff, 2009.
    [BibTeX] [PDF]
    [none]
    @Article{Kuemmerle2009,
    Title = {On measuring the accuracy of {SLAM} algorithms},
    Author = {R. Kuemmerle and B. Steder and C. Dornhege and M. Ruhnke and G. Grisetti and C. Stachniss and A. Kleiner},
    Journal = auro,
    Year = {2009},
    Pages = {387ff},
    Volume = {27},
    Abstract = {[none]},
    Issue = {4},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/kuemmerle09auro.pdf}
    }

  • A. Schneider, S. J. C. Stachniss, M. Reisert, H. Burkhardt, and W. Burgard, “Object Identification with Tactile Sensors Using Bag-of-Features,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2009.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Schneider2009,
    Title = {Object Identification with Tactile Sensors Using Bag-of-Features},
    Author = {A. Schneider and J. Sturm C. Stachniss and M. Reisert and H. Burkhardt and W. Burgard},
    Booktitle = iros,
    Year = {2009},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/wurm09iros.pdf}
    }

  • C. Stachniss, “Spatial Modeling and Robot Navigation,” Habilitation PhD Thesis, 2009.
    [BibTeX] [PDF]
    [none]
    @PhdThesis{Stachniss2009,
    Title = {Spatial Modeling and Robot Navigation},
    Author = {C. Stachniss},
    School = {University of Freiburg, Department of Computer Science},
    Year = {2009},
    Type = {Habilitation},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss-habil.pdf}
    }

  • C. Stachniss, Robotic Mapping and Exploration, Springer, 2009, vol. 55.
    [BibTeX]
    [none]
    @Book{Stachniss2009a,
    Title = {Robotic Mapping and Exploration},
    Author = {C. Stachniss},
    Publisher = {Springer},
    Year = {2009},
    Series = springerstaradvanced,
    Volume = {55},
    Abstract = {[none]},
    ISBN = {978-3-642-01096-5},
    Timestamp = {2014.04.24}
    }

  • C. Stachniss, O. Martinez Mozos, and W. Burgard, “Efficient Exploration of Unknown Indoor Environments using a Team of Mobile Robots,” Annals of Mathematics and Artificial Intelligence, vol. 52, p. 205ff, 2009.
    [BibTeX]
    [none]
    @Article{Stachniss2009b,
    Title = {Efficient Exploration of Unknown Indoor Environments using a Team of Mobile Robots},
    Author = {Stachniss, C. and Martinez Mozos, O. and Burgard, W.},
    Journal = {Annals of Mathematics and Artificial Intelligence},
    Year = {2009},
    Pages = {205ff},
    Volume = {52},
    Abstract = {[none]},
    Issue = {2},
    Timestamp = {2014.04.24}
    }

  • C. Stachniss, C. Plagemann, and A. J. Lilienthal, “Gas Distribution Modeling using Sparse Gaussian Process Mixtures,” Autonomous Robots, vol. 26, p. 187ff, 2009.
    [BibTeX]
    [none]
    @Article{Stachniss2009c,
    Title = {Gas Distribution Modeling using Sparse Gaussian Process Mixtures},
    Author = {Stachniss, C. and Plagemann, C. and Lilienthal, A.J.},
    Journal = auro,
    Year = {2009},
    Pages = {187ff},
    Volume = {26},
    Abstract = {[none]},
    Issue = {2},
    Timestamp = {2014.04.24}
    }

  • H. Strasdat, C. Stachniss, and W. Burgard, “Which Landmark is Useful? Learning Selection Policies for Navigation in Unknown Environments,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Kobe, Japan, 2009.
    [BibTeX]
    [none]
    @InProceedings{Strasdat2009,
    Title = {Which Landmark is Useful? Learning Selection Policies for Navigation in Unknown Environments},
    Author = {H. Strasdat and Stachniss, C. and Burgard, W.},
    Booktitle = icra,
    Year = {2009},
    Address = {Kobe, Japan},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • J. Sturm, V. Predeap, C. Stachniss, C. Plagemann, K. Konolige, and W. Burgard, “Learning Kinematic Models for Articulated Objects,” in Proceedings of the Int. Conf. on Artificial Intelligence (IJCAI) , Pasadena, CA, USA, 2009.
    [BibTeX]
    [none]
    @InProceedings{Sturm2009a,
    Title = {Learning Kinematic Models for Articulated Objects},
    Author = {J. Sturm and V. Predeap and Stachniss, C. and C. Plagemann and K. Konolige and Burgard, W.},
    Booktitle = ijcai,
    Year = {2009},
    Address = {Pasadena, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • J. Sturm, C. Stachniss, V. Predeap, C. Plagemann, K. Konolige, and W. Burgard, “Learning Kinematic Models for Articulated Objects,” in Online Proc. of the Learning Workshop (Snowbird) , Clearwater, FL, USA, 2009.
    [BibTeX]
    [none]
    @InProceedings{Sturm2009,
    Title = {Learning Kinematic Models for Articulated Objects},
    Author = {J. Sturm and Stachniss, C. and V. Predeap and C. Plagemann and K. Konolige and Burgard, W.},
    Booktitle = {Online Proc. of the Learning Workshop (Snowbird)},
    Year = {2009},
    Address = {Clearwater, FL, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • J. Sturm, C. Stachniss, V. Predeap, C. Plagemann, K. Konolige, and W. Burgard, “Towards Understanding Articulated Objects,” in Workshop Integrating Mobility and Manipulation at Robotics: Science and Systems (RSS) , Seattle, WA, USA, 2009.
    [BibTeX]
    [none]
    @InProceedings{Sturm2009b,
    Title = {Towards Understanding Articulated Objects},
    Author = {J. Sturm and Stachniss, C. and V. Predeap and C. Plagemann and K. Konolige and Burgard, W.},
    Booktitle = {Workshop Integrating Mobility and Manipulation at Robotics: Science and Systems (RSS)},
    Year = {2009},
    Address = {Seattle, WA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • K. M. Wurm, R. Kuemmerle, C. Stachniss, and W. Burgard, “Improving Robot Navigation in Structured Outdoor Environments by Identifying Vegetation from Laser Data,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2009.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Wurm2009,
    Title = {Improving Robot Navigation in Structured Outdoor Environments by Identifying Vegetation from Laser Data},
    Author = {K.M. Wurm and R. Kuemmerle and Stachniss, C. and Burgard, W.},
    Booktitle = iros,
    Year = {2009},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/wurm09iros.pdf}
    }

2008

  • B. Frank, M. Becker, C. Stachniss, M. Teschner, and W. Burgard, “Learning Cost Functions for Mobile Robot Navigation in Environments with Deformable Objects,” in Workshop on Path Planning on Cost Maps at the IEEE Int. Conf. on Robotics & Automation (ICRA) , Pasadena, CA, USA, 2008.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Frank2008,
    Title = {Learning Cost Functions for Mobile Robot Navigation in Environments with Deformable Objects},
    Author = {Frank, B. and Becker, M. and Stachniss, C. and Teschner, M. and Burgard, W.},
    Booktitle = icrawsplanning,
    Year = {2008},
    Address = {Pasadena, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/frank08icraws.pdf}
    }

  • B. Frank, M. Becker, C. Stachniss, M. Teschner, and W. Burgard, “Efficient Path Planning for Mobile Robots in Environments with Deformable Objects,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Pasadena, CA, USA, 2008.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Frank2008a,
    Title = {Efficient Path Planning for Mobile Robots in Environments with Deformable Objects},
    Author = {Frank, B. and Becker, M. and Stachniss, C. and Teschner, M. and Burgard, W.},
    Booktitle = ICRA,
    Year = {2008},
    Address = {Pasadena, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/frank08icra.pdf}
    }

  • G. Grisetti, D. Lordi Rizzini, C. Stachniss, E. Olson, and W. Burgard, “Online Constraint Network Optimization for Efficient Maximum Likelihood Map Learning,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Pasadena, CA, USA, 2008.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Grisetti2008,
    Title = {Online Constraint Network Optimization for Efficient Maximum Likelihood Map Learning},
    Author = {Grisetti, G. and Lordi Rizzini, D. and Stachniss, C. and Olson, E. and Burgard, W.},
    Booktitle = ICRA,
    Year = {2008},
    Address = {Pasadena, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/grisetti08icra.pdf}
    }

  • H. Kretzschmar, C. Stachniss, C. Plagemann, and W. Burgard, “Estimating Landmark Locations from Geo-Referenced Photographs,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Nice, France, 2008.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Kretzschmar2008,
    Title = {Estimating Landmark Locations from Geo-Referenced Photographs},
    Author = {Kretzschmar, H. and Stachniss, C. and Plagemann, C. and W. Burgard},
    Booktitle = iros,
    Year = {2008},
    Address = {Nice, France},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/kretzschmar08iros.pdf}
    }

  • J. Müller, C. Stachniss, K. O. Arras, and W. Burgard, “Socially Inspired Motion Planning for Mobile Robots in Populated Environments,” in International Conference on Cognitive Systems (CogSys) , Baden Baden, Germany, 2008.
    [BibTeX]
    [none]
    @InProceedings{Muller2008,
    Title = {Socially Inspired Motion Planning for Mobile Robots in Populated Environments},
    Author = {M\"uller, J. and Stachniss, C. and Arras, K.O. and Burgard, W.},
    Booktitle = COGSYS,
    Year = {2008},
    Address = {Baden Baden, Germany},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • P. Pfaff, C. Stachniss, C. Plagemann, and W. Burgard, “Efficiently Learning High-dimensional Observation Models for Monte-Carlo Localization using Gaussian Mixtures,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Nice, France, 2008.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Pfaff2008,
    Title = {Efficiently Learning High-dimensional Observation Models for Monte-Carlo Localization using Gaussian Mixtures},
    Author = {Pfaff, P. and Stachniss, C. and Plagemann, C. and W. Burgard},
    Booktitle = iros,
    Year = {2008},
    Address = {Nice, France},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/pfaff08iros.pdf}
    }

  • C. Plagemann, F. Endres, J. Hess, C. Stachniss, and W. Burgard, “Monocular Range Sensing: A Non-Parametric Learning Approach,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Pasadena, CA, USA, 2008.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Plagemann2008,
    Title = {Monocular Range Sensing: A Non-Parametric Learning Approach},
    Author = {Plagemann, C. and Endres, F. and Hess, J. and Stachniss, C. and Burgard, W.},
    Booktitle = ICRA,
    Year = {2008},
    Address = {Pasadena, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/plagemann08icra.pdf}
    }

  • C. Stachniss, M. Bennewitz, G. Grisetti, S. Behnke, and W. Burgard, “How to Learn Accurate Grid Maps with a Humanoid,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Pasadena, CA, USA, 2008.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2008,
    Title = {How to Learn Accurate Grid Maps with a Humanoid},
    Author = {Stachniss, C. and Bennewitz, M. and Grisetti, G. and Behnke, S. and Burgard, W.},
    Booktitle = ICRA,
    Year = {2008},
    Address = {Pasadena, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss08icra.pdf}
    }

  • C. Stachniss, C. Plagemann, A. Lilienthal, and W. Burgard, “Gas Distribution Modeling using Sparse Gaussian Process Mixture Models,” in Proceedings of Robotics: Science and Systems (RSS) , Zurich, Switzerland, 2008.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2008a,
    Title = {Gas Distribution Modeling using Sparse Gaussian Process Mixture Models},
    Author = {Stachniss, C. and Plagemann, C. and Lilienthal, A. and Burgard, W.},
    Booktitle = RSS,
    Year = {2008},
    Address = {Zurich, Switzerland},
    Note = {To appear},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss08rss.pdf}
    }

  • B. Steder, G. Grisetti, C. Stachniss, and W. Burgard, “Learning Visual Maps using Cameras and Inertial Sensors,” in Workshop on Robotic Perception, International Conference on Computer Vision Theory and Applications , Funchal, Madeira, Portugal, 2008.
    [BibTeX]
    [none]
    @InProceedings{Steder2008,
    Title = {Learning Visual Maps using Cameras and Inertial Sensors},
    Author = {Steder, B. and Grisetti, G. and Stachniss, C. and Burgard, W.},
    Booktitle = {Workshop on Robotic Perception, International Conference on Computer Vision Theory and Applications},
    Year = {2008},
    Address = {Funchal, Madeira, Portugal},
    Note = {To appear},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • K. M. Wurm, C. Stachniss, and W. Burgard, “Coordinated Multi-Robot Exploration using a Segmentation of the Environment,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Nice, France, 2008.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Wurm2008,
    Title = {Coordinated Multi-Robot Exploration using a Segmentation of the Environment},
    Author = {K.M. Wurm and Stachniss, C. and W. Burgard},
    Booktitle = iros,
    Year = {2008},
    Address = {Nice, France},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/wurm08iros.pdf}
    }

2007

  • W. Burgard, C. Stachniss, and D. Haehnel, “Mobile Robot Map Learning from Range Data in Dynamic Environments,” in Autonomous Navigation in Dynamic Environments, C. Laugier and R. Chatila, Eds., Springer, 2007, vol. 35.
    [BibTeX]
    [none]
    @InCollection{Burgard2007,
    Title = {Mobile Robot Map Learning from Range Data in Dynamic Environments},
    Author = {Burgard, W. and Stachniss, C. and Haehnel, D.},
    Booktitle = {Autonomous Navigation in Dynamic Environments},
    Publisher = springer,
    Year = {2007},
    Editor = {Laugier, C. and Chatila, R.},
    Series = springerstaradvanced,
    Volume = {35},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • G. Grisetti, S. Grzonka, C. Stachniss, P. Pfaff, and W. Burgard, “Efficient Estimation of Accurate Maximum Likelihood Maps in 3D,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , San Diego, CA, USA, 2007.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Grisetti2007c,
    Title = {Efficient Estimation of Accurate Maximum Likelihood Maps in 3D},
    Author = {Grisetti, G. and Grzonka, S. and Stachniss, C. and Pfaff, P. and Burgard, W.},
    Booktitle = iros,
    Year = {2007},
    Address = {San Diego, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/grisetti07iros.pdf}
    }

  • G. Grisetti, C. Stachniss, and W. Burgard, “Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters,” IEEE Transactions on Robotics, vol. 23, iss. 1, pp. 34-46, 2007.
    [BibTeX] [PDF]
    [none]
    @Article{Grisetti2007a,
    Title = {Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters},
    Author = {Grisetti, G. and Stachniss, C. and Burgard, W.},
    Journal = ieeetransrob,
    Year = {2007},
    Number = {1},
    Pages = {34--46},
    Volume = {23},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/grisetti07tro.pdf}
    }

  • G. Grisetti, C. Stachniss, S. Grzonka, and W. Burgard, “A Tree Parameterization for Efficiently Computing Maximum Likelihood Maps using Gradient Descent,” in Proceedings of Robotics: Science and Systems (RSS) , Atlanta, GA, USA, 2007.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Grisetti2007b,
    Title = {A Tree Parameterization for Efficiently Computing Maximum Likelihood Maps using Gradient Descent},
    Author = {Grisetti, G. and Stachniss, C. and Grzonka, S. and Burgard, W.},
    Booktitle = RSS,
    Year = {2007},
    Address = {Atlanta, GA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/grisetti07rss.pdf}
    }

  • G. Grisetti, G. D. Tipaldi, C. Stachniss, W. Burgard, and D. Nardi, “Fast and Accurate SLAM with Rao-Blackwellized Particle Filters,” Robotics and Autonomous Systems, vol. 55, iss. 1, pp. 30-38, 2007.
    [BibTeX] [PDF]
    [none]
    @Article{Grisetti2007,
    Title = {Fast and Accurate {SLAM} with Rao-Blackwellized Particle Filters},
    Author = {Grisetti, G. and Tipaldi, G.D. and Stachniss, C. and Burgard, W. and Nardi, D.},
    Journal = jras,
    Year = {2007},
    Number = {1},
    Pages = {30--38},
    Volume = {55},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/grisetti07jras.pdf}
    }

  • D. Joho, C. Stachniss, P. Pfaff, and W. Burgard, “Autonomous Exploration for 3D Map Learning,” in Autonome Mobile Systeme , Kaiserslautern, Germany, 2007.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Joho2007,
    Title = {Autonomous Exploration for 3D Map Learning},
    Author = {Joho, D. and Stachniss, C. and Pfaff, P. and Burgard, W.},
    Booktitle = AMS,
    Year = {2007},
    Address = {Kaiserslautern, Germany},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/joho07ams.pdf}
    }

  • O. Martínez-Mozos, C. Stachniss, A. Rottmann, and W. Burgard, “Using AdaBoost for Place Labelling and Topological Map Building,” in Robotics Research, S. Thrun, R. Brooks, and H. Durrant-Whyte, Eds., Springer, 2007, vol. 28.
    [BibTeX] [PDF]
    [none]
    @InCollection{Mart'inez-Mozos2007,
    Title = {Using AdaBoost for Place Labelling and Topological Map Building},
    Author = {Mart\'{i}nez-Mozos, O. and Stachniss, C. and Rottmann, A. and Burgard, W.},
    Booktitle = {Robotics Research},
    Publisher = springer,
    Year = {2007},
    Editor = {Thrun, S. and Brooks, R. and Durrant-Whyte, H.},
    Series = springerstaradvanced,
    Volume = {28},
    Abstract = {[none]},
    ISBN = {978-3-540-48110-2},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/martinez07springer.pdf}
    }

  • P. Pfaff, R. Kuemmerle, D. Joho, C. Stachniss, R. Triebel, and Burgard, “Navigation in Combined Outdoor and Indoor Environments using Multi-Level Surface Maps,” in Workshop on Safe Navigation in Open and Dynamic Environments at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , San Diego, CA, USA, 2007.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Pfaff2007a,
    Title = {Navigation in Combined Outdoor and Indoor Environments using Multi-Level Surface Maps},
    Author = {Pfaff, P. and Kuemmerle, R. and Joho, D. and Stachniss, C. and Triebel, R. and Burgard},
    Booktitle = iroswsnav,
    Year = {2007},
    Address = {San Diego, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/pfaff07irosws.pdf}
    }

  • P. Pfaff, R. Triebel, C. Stachniss, P. Lamon, W. Burgard, and R. Siegwart, “Towards Mapping of Cities,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Rome, Italy, 2007.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Pfaff2007,
    Title = {Towards Mapping of Cities},
    Author = {Pfaff, P. and Triebel, R. and Stachniss, C. and Lamon, P. and Burgard, W. and Siegwart, R.},
    Booktitle = icra,
    Year = {2007},
    Address = {Rome, Italy},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/pfaff07icra.pdf}
    }

  • C. Stachniss, G. Grisetti, W. Burgard, and N. Roy, “Evaluation of Gaussian Proposal Distributions for Mapping with Rao-Blackwellized Particle Filters,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , San Diego, CA, USA, 2007.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2007a,
    Title = {Evaluation of Gaussian Proposal Distributions for Mapping with Rao-Blackwellized Particle Filters},
    Author = {Stachniss, C. and Grisetti, G. and Burgard, W. and Roy, N.},
    Booktitle = iros,
    Year = {2007},
    Address = {San Diego, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss07iros.pdf}
    }

  • C. Stachniss, G. Grisetti, O. Martínez-Mozos, and W. Burgard, “Efficiently Learning Metric and Topological Maps with Autonomous Service Robots,” it — Information Technology, vol. 49, iss. 4, pp. 232-238, 2007.
    [BibTeX]
    [none]
    @Article{Stachniss2007,
    Title = {Efficiently Learning Metric and Topological Maps with Autonomous Service Robots},
    Author = {Stachniss, C. and Grisetti, G. and Mart\'{i}nez-Mozos, O. and Burgard, W.},
    Journal = {it -- Information Technology},
    Year = {2007},
    Number = {4},
    Pages = {232--238},
    Volume = {49},
    Abstract = {[none]},
    Editor = {Buss, M. and Lawitzki, G.},
    Timestamp = {2014.04.24}
    }

  • B. Steder, G. Grisetti, S. Grzonka, C. Stachniss, A. Rottmann, and W. Burgard, “Learning Maps in 3D using Attitude and Noisy Vision Sensors,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , San Diego, CA, USA, 2007.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Steder2007,
    Title = {Learning Maps in 3D using Attitude and Noisy Vision Sensors},
    Author = {Steder, B. and Grisetti, G. and Grzonka, S. and Stachniss, C. and Rottmann, A. and Burgard, W.},
    Booktitle = iros,
    Year = {2007},
    Address = {San Diego, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/steder07iros.pdf}
    }

  • B. Steder, A. Rottmann, G. Grisetti, C. Stachniss, and W. Burgard, “Autonomous Navigation for Small Flying Vehicles,” in Workshop on Micro Aerial Vehicles at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , San Diego, CA, USA, 2007.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Steder,
    Title = {Autonomous Navigation for Small Flying Vehicles},
    Author = {Steder, B. and Rottmann, A. and Grisetti, G. and Stachniss, C. and Burgard, W.},
    Booktitle = iroswsfly,
    Year = {2007},
    Address = {San Diego, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~steder/publications/steder07irosws.pdf}
    }

  • H. Strasdat, C. Stachniss, M. Bennewitz, and W. Burgard, “Visual Bearing-Only Simultaneous Localization and Mapping with Improved Feature Matching,” in Autonome Mobile Systeme , Kaiserslautern, Germany, 2007.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Strasdat2007,
    Title = {Visual Bearing-Only Simultaneous Localization and Mapping with Improved Feature Matching},
    Author = {Strasdat, H. and Stachniss, C. and Bennewitz, M. and Burgard, W.},
    Booktitle = AMS,
    Year = {2007},
    Address = {Kaiserslautern, Germany},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/strasdat07ams.pdf}
    }

  • K. M. Wurm, C. Stachniss, G. Grisetti, and W. Burgard, “Improved Simultaneous Localization and Mapping using a Dual Representation of the Environment,” in Proceedings of the European Conference on Mobile Robots (ECMR) , Freiburg, Germany, 2007.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Wurm2007,
    Title = {Improved Simultaneous Localization and Mapping using a Dual Representation of the Environment},
    Author = {Wurm, K.M. and Stachniss, C. and Grisetti, G. and Burgard, W.},
    Booktitle = ECMR,
    Year = {2007},
    Address = {Freiburg, Germany},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/wurm07ecmr.pdf}
    }

2006

  • M. Bennewitz, C. Stachniss, W. Burgard, and S. Behnke, “Metric Localization with Scale-Invariant Visual Features using a Single Perspective Camera,” in European Robotics Symposium 2006 , 2006, pp. 143-157.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Bennewitz2006,
    Title = {Metric Localization with Scale-Invariant Visual Features using a Single Perspective Camera},
    Author = {Bennewitz, M. and Stachniss, C. and Burgard, W. and Behnke, S.},
    Booktitle = {European Robotics Symposium 2006},
    Year = {2006},
    Editor = {H.I. Christiensen},
    Pages = {143--157},
    Publisher = {Springer-Verlag Berlin Heidelberg, Germany},
    Series = springerstaradvanced,
    Volume = {22},
    Abstract = {[none]},
    ISBN = {3-540-32688-X},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/bennewitz06euros.pdf}
    }

  • A. Gil, O. Reinoso, O. Martínez-Mozos, C. Stachniss, and W. Burgard, “Improving Data Association in Vision-based SLAM,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Beijing, China, 2006.
    [BibTeX]
    [none]
    @InProceedings{Gil2006,
    Title = {Improving Data Association in Vision-based {SLAM}},
    Author = {Gil, A. and Reinoso, O. and Mart\'{i}nez-Mozos, O. and Stachniss, C. and Burgard, W.},
    Booktitle = iros,
    Year = {2006},
    Address = {Beijing, China},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

  • G. Grisetti, G. D. Tipaldi, C. Stachniss, W. Burgard, and D. Nardi, “Speeding-Up Rao-Blackwellized SLAM,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Orlando, FL, USA, 2006, pp. 442-447.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Grisetti2006,
    Title = {Speeding-Up Rao-Blackwellized {SLAM}},
    Author = {Grisetti, G. and Tipaldi, G.D. and Stachniss, C. and Burgard, W. and Nardi, D.},
    Booktitle = icra,
    Year = {2006},
    Address = {Orlando, FL, USA},
    Pages = {442--447},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/grisetti06icra.pdf}
    }

  • P. Lamon, C. Stachniss, R. Triebel, P. Pfaff, C. Plagemann, G. Grisetti, S. Kolski, W. Burgard, and R. Siegwart, “Mapping with an Autonomous Car,” in Workshop on Safe Navigation in Open and Dynamic Environments at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Beijing, China, 2006.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Lamon2006,
    Title = {Mapping with an Autonomous Car},
    Author = {Lamon, P. and Stachniss, C. and Triebel, R. and Pfaff, P. and Plagemann, C. and Grisetti, G. and Kolski, S. and Burgard, W. and Siegwart, R.},
    Booktitle = iroswsnav,
    Year = {2006},
    Address = {Beijing, China},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/lamon06iros.pdf}
    }

  • D. Meier, C. Stachniss, and W. Burgard, “Cooperative Exploration With Multiple Robots Using Low Bandwidth Communication,” in Informationsfusion in der Mess- und Sensortechnik , 2006, pp. 145-157.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Meier2006,
    Title = {Cooperative Exploration With Multiple Robots Using Low Bandwidth Communication},
    Author = {Meier, D. and Stachniss, C. and Burgard, W.},
    Booktitle = {Informationsfusion in der Mess- und Sensortechnik},
    Year = {2006},
    Editor = {Beyerer, J. and Puente Le\'{o}n, F. and Sommer, K.-D.},
    Pages = {145--157},
    Abstract = {[none]},
    ISBN = {3-86644-053-7},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/meier06sensor.pdf}
    }

  • C. Plagemann, C. Stachniss, and W. Burgard, “Efficient Failure Detection for Mobile Robots using Mixed-Abstraction Particle Filters,” in European Robotics Symposium 2006 , 2006, pp. 93-107.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Plagemann2006,
    Title = {Efficient Failure Detection for Mobile Robots using Mixed-Abstraction Particle Filters},
    Author = {Plagemann, C. and Stachniss, C. and Burgard, W.},
    Booktitle = {European Robotics Symposium 2006},
    Year = {2006},
    Editor = {H.I. Christiensen},
    Pages = {93--107},
    Publisher = {Springer-Verlag Berlin Heidelberg, Germany},
    Series = springerstaradvanced,
    Volume = {22},
    Abstract = {[none]},
    ISBN = {3-540-32688-X},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/plagemann06euros.pdf}
    }

  • D. Sonntag, S. Stachniss-Carp, C. Stachniss, and V. Stachniss, “Determination of Root Canal Curvatures before and after Canal Preparation (Part II): A Method based on Numeric Calculus,” Aust Endod J, vol. 32, pp. 16-25, 2006.
    [BibTeX] [PDF]
    [none]
    @Article{Sonntag2006,
    Title = {Determination of Root Canal Curvatures before and after Canal Preparation (Part II): A Method based on Numeric Calculus},
    Author = {Sonntag, D. and Stachniss-Carp, S. and Stachniss, C. and Stachniss, V.},
    Journal = {Aust Endod J},
    Year = {2006},
    Pages = {16--25},
    Volume = {32},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/sonntag06endod.pdf}
    }

  • C. Stachniss, “Exploration and Mapping with Mobile Robots,” PhD Thesis, 2006.
    [BibTeX] [PDF]
    [none]
    @PhdThesis{Stachniss2006a,
    Title = {Exploration and Mapping with Mobile Robots},
    Author = {Stachniss, C.},
    School = {University of Freiburg, Department of Computer Science},
    Year = {2006},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss06phd.pdf}
    }

  • C. Stachniss, O. Martínez-Mozos, and W. Burgard, “Speeding-Up Multi-Robot Exploration by Considering Semantic Place Information,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Orlando, FL, USA, 2006, pp. 1692-1697.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2006,
    Title = {Speeding-Up Multi-Robot Exploration by Considering Semantic Place Information},
    Author = {Stachniss, C. and Mart\'{i}nez-Mozos, O. and Burgard, W.},
    Booktitle = icra,
    Year = {2006},
    Address = {Orlando, FL, USA},
    Pages = {1692--1697},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss06icra.pdf}
    }

2005

  • W. Burgard, M. Moors, C. Stachniss, and F. Schneider, “Coordinated Multi-Robot Exploration,” IEEE Transactions on Robotics, vol. 21, iss. 3, pp. 376-378, 2005.
    [BibTeX] [PDF]
    [none]
    @Article{Burgard2005a,
    Title = {Coordinated Multi-Robot Exploration},
    Author = {W. Burgard and M. Moors and C. Stachniss and F. Schneider},
    Journal = ieeetransrob,
    Year = {2005},
    Number = {3},
    Pages = {376--378},
    Volume = {21},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/burgard05tro.pdf}
    }

  • W. Burgard, C. Stachniss, and G. Grisetti, “Information Gain-based Exploration Using Rao-Blackwellized Particle Filters,” in Proc. of the Learning Workshop (Snowbird) , Snowbird, UT, USA, 2005.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Burgard2005,
    Title = {Information Gain-based Exploration Using Rao-Blackwellized Particle Filters},
    Author = {Burgard, W. and Stachniss, C. and Grisetti, G.},
    Booktitle = {Proc. of the Learning Workshop (Snowbird)},
    Year = {2005},
    Address = {Snowbird, UT, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/burgard05snowbird.pdf}
    }

  • G. Grisetti, C. Stachniss, and W. Burgard, “Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Barcelona, Spain, 2005, pp. 2443-2448.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Grisetti2005,
    Title = {Improving Grid-based {SLAM} with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling},
    Author = {Grisetti, G. and Stachniss, C. and Burgard, W.},
    Booktitle = ICRA,
    Year = {2005},
    Address = {Barcelona, Spain},
    Pages = {2443--2448},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/grisetti05icra.pdf}
    }

  • O. Martínez-Mozos, C. Stachniss, and W. Burgard, “Supervised Learning of Places from Range Data using Adaboost,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Barcelona, Spain, 2005, pp. 1742-1747.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Mart'inez-Mozos2005,
    Title = {Supervised Learning of Places from Range Data using Adaboost},
    Author = {Mart\'{i}nez-Mozos, O. and Stachniss, C. and W. Burgard},
    Booktitle = ICRA,
    Year = {2005},
    Address = {Barcelona, Spain},
    Pages = {1742--1747},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/martinez05icra.pdf}
    }

  • D. Meier, C. Stachniss, and W. Burgard, “Coordinating Multiple Robots During Exploration Under Communication With Limited Bandwidth,” in Proceedings of the European Conference on Mobile Robots (ECMR) , Ancona, Italy, 2005, pp. 26-31.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Meier2005,
    Title = {Coordinating Multiple Robots During Exploration Under Communication With Limited Bandwidth},
    Author = {Meier, D. and Stachniss, C. and Burgard, W.},
    Booktitle = ECMR,
    Year = {2005},
    Address = {Ancona, Italy},
    Pages = {26--31},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/meier05ecmr.pdf}
    }

  • A. Rottmann, O. Martínez-Mozos, C. Stachniss, and W. Burgard, “Place Classification of Indoor Environments with Mobile Robots using Boosting,” in Proceedings of the National Conference on Artificial Intelligence (AAAI) , Pittsburgh, PA, USA, 2005, pp. 1306-1311.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Rottmann2005,
    Title = {Place Classification of Indoor Environments with Mobile Robots using Boosting},
    Author = {Rottmann, A. and Mart\'{i}nez-Mozos, O. and Stachniss, C. and Burgard, W.},
    Booktitle = aaai,
    Year = {2005},
    Address = {Pittsburgh, PA, USA},
    Pages = {1306--1311},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/rottmann05aaai.pdf}
    }

  • C. Stachniss and W. Burgard, “Mobile Robot Mapping and Localization in Non-Static Environments,” in Proceedings of the National Conference on Artificial Intelligence (AAAI) , Pittsburgh, PA, USA, 2005, pp. 1324-1329.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2005,
    Title = {Mobile Robot Mapping and Localization in Non-Static Environments},
    Author = {Stachniss, C. and Burgard, W.},
    Booktitle = aaai,
    Year = {2005},
    Address = {Pittsburgh, PA, USA},
    Pages = {1324--1329},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss05aaai.pdf}
    }

  • C. Stachniss, G. Grisetti, and W. Burgard, “Information Gain-based Exploration Using Rao-Blackwellized Particle Filters,” in Proceedings of Robotics: Science and Systems (RSS) , Cambridge, MA, USA, 2005, pp. 65-72.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2005a,
    Title = {Information Gain-based Exploration Using Rao-Blackwellized Particle Filters},
    Author = {Stachniss, C. and Grisetti, G. and Burgard, W.},
    Booktitle = RSS,
    Year = {2005},
    Address = {Cambridge, MA, USA},
    Pages = {65--72},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss05rss.pdf}
    }

  • C. Stachniss, G. Grisetti, and W. Burgard, “Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM after Actively Closing Loops,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , Barcelona, Spain, 2005, pp. 667-672.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2005d,
    Title = {Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for {SLAM} after Actively Closing Loops},
    Author = {Stachniss, C. and Grisetti, G. and Burgard, W.},
    Booktitle = ICRA,
    Year = {2005},
    Address = {Barcelona, Spain},
    Pages = {667--672},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss05icra.pdf}
    }

  • C. Stachniss, D. Hähnel, W. Burgard, and G. Grisetti, “On Actively Closing Loops in Grid-based FastSLAM,” Advanced Robotics, vol. 19, iss. 10, pp. 1059-1080, 2005.
    [BibTeX] [PDF]
    [none]
    @Article{Stachniss2005c,
    Title = {On Actively Closing Loops in Grid-based {FastSLAM}},
    Author = {Stachniss, C. and H\"{a}hnel, D. and Burgard, W. and Grisetti, G.},
    Journal = advancedrobotics,
    Year = {2005},
    Number = {10},
    Pages = {1059--1080},
    Volume = {19},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss05ar.pdf}
    }

  • C. Stachniss, O. Martínez-Mozos, A. Rottmann, and W. Burgard, “Semantic Labeling of Places,” in Proceedings of the Int. Symposium of Robotics Research (ISRR) , San Francisco, CA, USA, 2005.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2005b,
    Title = {Semantic Labeling of Places},
    Author = {Stachniss, C. and Mart\'{i}nez-Mozos, O. and Rottmann, A. and Burgard, W.},
    Booktitle = isrr,
    Year = {2005},
    Address = {San Francisco, CA, USA},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss05isrr.pdf}
    }

  • P. Trahanias, W. Burgard, A. Argyros, D. Hähnel, H. Baltzakis, P. Pfaff, and C. Stachniss, “TOURBOT and WebFAIR: Web-Operated Mobile Robots for Tele-Presence in Populated Exhibitions,” IEEE Robotics & Automation Magazine, vol. 12, iss. 2, pp. 77-89, 2005.
    [BibTeX] [PDF]
    [none]
    @Article{Trahanias2005,
    Title = {{TOURBOT} and {WebFAIR}: Web-Operated Mobile Robots for Tele-Presence in Populated Exhibitions},
    Author = {Trahanias, P. and Burgard, W. and Argyros, A. and H\"{a}hnel, D. and Baltzakis, H. and Pfaff, P. and Stachniss, C.},
    Journal = {IEEE Robotics \& Automation Magazine},
    Year = {2005},
    Number = {2},
    Pages = {77--89},
    Volume = {12},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://ieeexplore.ieee.org/iel5/100/31383/01458329.pdf?arnumber=1458329}
    }

2004

  • C. Stachniss, G. Grisetti, D. Hähnel, and W. Burgard, “Improved Rao-Blackwellized Mapping by Adaptive Sampling and Active Loop-Closure,” in Proceedings of the Workshop on Self-Organization of AdaptiVE behavior (SOAVE) , Ilmenau, Germany, 2004, pp. 1-15.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2004a,
    Title = {Improved Rao-Blackwellized Mapping by Adaptive Sampling and Active Loop-Closure},
    Author = {Stachniss, C. and Grisetti, G. and H\"{a}hnel, D. and Burgard, W.},
    Booktitle = SOAVE,
    Year = {2004},
    Address = {Ilmenau, Germany},
    Pages = {1--15},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss04soave.pdf}
    }

  • C. Stachniss, D. Hähnel, and W. Burgard, “Exploration with Active Loop-Closing for FastSLAM,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Sendai, Japan, 2004, pp. 1505-1510.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2004,
    Title = {Exploration with Active Loop-Closing for {FastSLAM}},
    Author = {Stachniss, C. and H\"{a}hnel, D. and Burgard, W.},
    Booktitle = IROS,
    Year = {2004},
    Address = {Sendai, Japan},
    Pages = {1505--1510},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss04iros.pdf}
    }

2003

  • C. Stachniss and W. Burgard, “Exploring Unknown Environments with Mobile Robots using Coverage Maps,” in Proceedings of the Int. Conf. on Artificial Intelligence (IJCAI) , Acapulco, Mexico, 2003, pp. 1127-1132.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2003,
    Title = {Exploring Unknown Environments with Mobile Robots using Coverage Maps},
    Author = {Stachniss, C. and Burgard, W.},
    Booktitle = IJCAI,
    Year = {2003},
    Address = {Acapulco, Mexico},
    Pages = {1127--1132},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss03ijcai.pdf}
    }

  • C. Stachniss and W. Burgard, “Using Coverage Maps to Represent the Environment of Mobile Robots,” in Proceedings of the European Conference on Mobile Robots (ECMR) , Radziejowice, Poland, 2003, pp. 59-64.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2003a,
    Title = {Using Coverage Maps to Represent the Environment of Mobile Robots},
    Author = {Stachniss, C. and Burgard, W.},
    Booktitle = ECMR,
    Year = {2003},
    Address = {Radziejowice, Poland},
    Pages = {59--64},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss03ecmr.pdf}
    }

  • C. Stachniss and W. Burgard, “Mapping and Exploration with Mobile Robots using Coverage Maps,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Las Vegas, NV, USA, 2003, pp. 476-481.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2003b,
    Title = {Mapping and Exploration with Mobile Robots using Coverage Maps},
    Author = {Stachniss, C. and Burgard, W.},
    Booktitle = IROS,
    Year = {2003},
    Address = {Las Vegas, NV, USA},
    Pages = {476--481},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss03iros.pdf}
    }

  • C. Stachniss, D. Hähnel, and W. Burgard, “Grid-based FastSLAM and Exploration with Active Loop Closing,” in Online Proceedings of the Dagstuhl Seminar on Robot Navigation (Dagstuhl Seminar 03501) , Dagstuhl, Germany, 2003.
    [BibTeX]
    [none]
    @InProceedings{Stachniss2003c,
    Title = {Grid-based {FastSLAM} and Exploration with Active Loop Closing},
    Author = {Stachniss, C. and H\"{a}hnel, D. and Burgard, W.},
    Booktitle = {Online Proceedings of the Dagstuhl Seminar on Robot Navigation (Dagstuhl Seminar 03501)},
    Year = {2003},
    Address = {Dagstuhl, Germany},
    Abstract = {[none]},
    Timestamp = {2014.04.24}
    }

2002

  • C. Stachniss, “Zielgerichtete Kollisionsvermeidung für mobile Roboter in dynamischen Umgebungen,” Master Thesis, 2002.
    [BibTeX] [PDF]
    [none]
    @MastersThesis{Stachniss2002,
    Title = {{Z}ielgerichtete {K}ollisionsvermeidung f{\"u}r mobile {R}oboter in dynamischen {U}mgebungen},
    Author = {Stachniss, C.},
    School = {University of Freiburg, Department of Computer Science},
    Year = {2002},
    Note = {In German},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss02diplom.pdf}
    }

  • C. Stachniss and W. Burgard, “An Integrated Approach to Goal-directed Obstacle Avoidance under Dynamic Constraints for Dynamic Environments,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Lausanne, Switzerland, 2002, pp. 508-513.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Stachniss2002a,
    Title = {An Integrated Approach to Goal-directed Obstacle Avoidance under Dynamic Constraints for Dynamic Environments},
    Author = {Stachniss, C. and Burgard, W.},
    Booktitle = IROS,
    Year = {2002},
    Address = {Lausanne, Switzerland},
    Pages = {508--513},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/stachniss02iros.pdf}
    }

igg
Institute of Geodesy
and Geoinformation
lwf
Faculty of Agriculture
ubn
University of Bonn