IROS 2020: F. Magistri: Segmentation-Based 4D Registration of Plants Point Clouds … (Trailer)
F. Magistri, N. Chebrolu, and C. Stachniss, “Segmentation-Based 4D Registration of Plants Point Clouds for Phenotyping,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020.
Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/magistri2020iros.pdf
IROS 2020: J. Behley: Domain Transfer for Semantic Segmentation of LiDAR Data using DNNs (Trailer)
F. Langer, A. Milioto, A. Haag, J. Behley, and C. Stachniss, “Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020.
Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/langer2020iros.pdf
IROS 2020: J. Behley: LiDAR Panoptic Segmentation for Autonomous Driving (Trailer)
Trailer video for the paper:
A. Milioto, J. Behley, C. McCool, and C. Stachniss, “LiDAR Panoptic Segmentation for Autonomous Driving,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020.
Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2020iros.pdf
IROS 2020: X. Chen: Learning an Overlap-based Observation Model for 3D LiDAR Localization (Trailer)
Trailer Video for the work: X. Chen, T. Läbe, L. Nardi, J. Behley, and C. Stachniss, “Learning an Overlap-based Observation Model for 3D LiDAR Localization,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020.
Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2020iros.pdf
Code available!
Join our conference DIGICROP 2020 for free!
Visit: https://www.digicrop.de
Join us for our free, online conference on Digital Technologies for Sustainable Crop Production run by the Cluster of Excellence PhenoRob.
DIGICROP’2020: Spatio-Temporal Registration of Plant Point Clouds by Chebrolu et al. (Trailer)
D. Gogoll – Unsupervised Domain Adaptation for Transferring Plant Classification Systems (Trailer)
Watch the full presentation: http://digicrop.de/program/unsupervised-domain-adaptation-for-transferring-plant-classification-systems-to-new-field-environments-crops-and-robots/
RSS’2020: OverlapNet – Loop Closing for LiDAR-based SLAM by X. Chen et al.
Paper:
X. Chen, T. Läbe, A. Milioto, T. Röhling, O. Vysotska, A. Haag, J. Behley, and C. Stachniss,
“OverlapNet: Loop Closing for LiDAR-based SLAM,”
in Proceedings of Robotics: Science and Systems (RSS), 2020.
Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2020rss.pdf
Code: https://github.com/PRBonn/OverlapNet (to be released before RSS)
Vide: https://youtu.be/YTfliBco6aw
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
IROS’2019 submission – Andres Milioto, Ignacio Vizzo, Jens Behley, Cyrill Stachniss.
Predictions from Sequence 13 Kitti dataset. Each frame is processed individually, and in under 100ms in a single GPU, under the frame rate of a Velodyne LiDAR scanner.
Code and data coming soon.
Resources:
CODE Slam [SuMa]: https://github.com/jbehley/SuMa
CODE Semantics [Lidar-Bonnetal]: https://github.com/PRBonn/lidar-bonnetal
Kitti dataset: http://www.cvlibs.net/datasets/kitti/
Semantic dataset: http://semantic-kitti.org
We thank NVIDIA Corporation for providing a Quadro P6000 GPU used to support this research.
ICRA’2020: Visual Servoing-based Navigation for Monitoring Crop Row Fields by Ahmadi et al.
Visual Servoing-based Navigation for Monitoring Crop Row Fields by Ahmadi et al., ICRA 2020
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (ICCV’19)
With SemanticKITTI, we release a large dataset to propel research on laser-based semantic segmentation. We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete 360 deg field-of-view of the employed automotive LiDAR. We propose three benchmark tasks based on this dataset: (i) semantic segmentation of point clouds using a single scan, (ii) semantic segmentation using sequences comprised of multiple past scans, and (iii) semantic scene completion, which requires to anticipate the semantic scene in the future. We provide baseline experiments and show that there is a need for more sophisticated models to efficiently tackle these tasks.
See: http://semantic-kitti.org
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences,” in Proc. of the IEEE/CVF International Conf.~on Computer Vision (ICCV), 2019.
PDF: https://arxiv.org/pdf/1904.01416.pdf
Bonnetal – Semantic Segmentation People vs Background by A. Milioto et al.
Model was quantized to INT8 and calibrated for fast GPU inference using TensorRT.
Our framework allows for C++ inference (with or without ROS) of real-time CNNs for classification, semantic segmentation, and a variety of other tasks which are coming soon.
Code: https://github.com/PRBonn/bonnetal
Architecture: MobilenetsV2+ASPP
Dataset: COCO (only people) + Supervisely Persons
Credit Original Video: https://www.youtube.com/watch?v=OPf0YbXqDm0
Category
Science & Technology
Suggested by SME
Mark Ronson – Uptown Funk (Official Video) ft. Bruno Mars
Music in this video
Song
Uptown Funk (feat. Bruno Mars)
Artist
Mark Ronson
Album
NRJ Hit Music Only 2015
Writers
Charlie Wilson, Robert Wilson, Lonnie Simmons, Ronnie Wilson, Jeff Bhasker, Nicholas Williams, Mark Ronson, Rudolph Taylor, Philip Lawrence, Devon Gallaspy, Bruno Mars
Licensed to YouTube by
SME (on behalf of Warner Special Marketing (France)); LatinAutor – Warner Chappell, LatinAutor – PeerMusic, PEDL, Warner Chappell, LatinAutor – SonyATV, União Brasileira de Compositores, LatinAutor, UMPG Publishing, AMRA, New Songs Administration (Publisher), UMPI, Bicycle Music Co. (Publishing), LatinAutor – UMPG, Abramus Digital, CMRRA, UBEM, Broma 16, Global Music Rights LLC, ARESA, BMG Rights Management, Sony ATV Publishing, ASCAP, BMI – Broadcast Music Inc., Kobalt Music Publishing, SOLAR Music Rights Management, and 25 Music Rights Societies
ICRA’2019: Effective Visual Place Recognition Using Multi-Sequence Maps
O. Vysotska and C. Stachniss, “Effective Visual Place Recognition Using Multi-Sequence Maps,” IEEE Robotics and Automation Letters (RA-L) and presentation at ICRA, 2019.
PDF: http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/vysotska2019ral.pdf
ICRA’2019: Actively Improving Robot Navigation On Different Terrains Using GPMMs
L. Nardi and C. Stachniss, “Actively Improving Robot Navigation On Different Terrains Using Gaussian Process Mixture Models,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019.
http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/nardi2019icra-airn.pdf
ICRA’2019: Robot Localization Based on Aerial Images for Precision Agriculture
Robot Localization Based on Aerial Images for Precision Agriculture Tasks in Crop Fields
by N. Chebrolu, P. Lottes, T. Laebe, and C. Stachniss
In Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019.
Paper: http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chebrolu2019icra.pdf
ICRA’2019: Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs
L. Nardi and C. Stachniss, “Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs ,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019.
http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/nardi2019icra-uapp.pdf
IROS’19: ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras…
ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals
by Emanuele Palazzolo, Jens Behley, Philipp Lottes, Philippe Giguere, and Cyrill Stachniss
IROS 2019
Arxiv paper: https://arxiv.org/abs/1905.02082
Code release: https://github.com/PRBonn/refusion
IROS’19: SuMa++: Efficient LiDAR-based Semantic SLAM by Chen et al.
SuMa++: Efficient LiDAR-based Semantic SLAM
by Xieyuanli Chen, Andres Milioto, Emanuele Palazzolo, Philippe Giguere, Jens Behley, and Cyrill Stachniss
IROS 2019
Escarda Technologies GmbH Concept Video
IROS’2018 Workshop: Towards Uncertainty-Aware Path Planning for Navigation
L. Nardi and C. Stachniss, “Towards Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs,” in 10th Workshop on Planning, Perception and Navigation for Intelligent Vehicles at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2018.
RAL’2018: FCNs with Sequential Information for Robust Crop and Weed Detection
Trailer for the paper:
Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming by P. Lottes, J. Behley, A. Milioto, and C. Stachniss, RAL 2018
IROS’2018: Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment
Trailer for the paper:
Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming by P. Lottes, J. Behley, N. Chebrolu, A. Milioto, and C. Stachniss, IROS 2018.
IROS’2018: Joint Ego-motion Estimation Using a Laser Scanner and a Monocular Camera
Trailer for the paper:
Joint Ego-motion Estimation Using a Laser Scanner and a Monocular Camera Through Relative Orientation Estimation and 1-DoF ICP by K. Huang and C. Stachniss, IROS 2018
Andres about Semantic Segmentation in Brisbane
ICRA’2018: A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration
B. Della Corte, I. Bogoslavskyi, C. Stachniss, and G. Grisetti, “A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2018.
ICRA’2018: Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots …
A. Milioto, P. Lottes, and C. Stachniss, “Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs,” Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2018.
ICRA’2018: Fast Image-Based Geometric Change Detection Given a 3D Model
Video trailer for the paper:
E. Palazzolo and C. Stachniss, “Fast Image-Based Geometric Change Detection Given a 3D Model,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2018.
Paper: http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/palazzolo2018icra.pdf
Code: https://github.com/Photogrammetry-Robotics-Bonn/fast_change_detection/
Data: http://www.ipb.uni-bonn.de/data/changedetection2017/
ICRA’2018: On Geometric Models and Their Accuracy for Extrinsic Sensor Calibration
Trailer for the paper
On Geometric Models and Their Accuracy for Extrinsic Sensor Calibration by K. Huang and C. Stachniss, ICRA 2018
Bonnet: Prediction of Cityscapes
Bonnet: Prediction of Cityscapes Test Sequences 00, 01, and 02 with 1024x512px Input.
Based on: A. Milioto and C. Stachniss, “Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs”, in arXiv, 1802.08960, 2018
ICRA’18: Flexible Multi-Cue Photometric Point Cloud Registration (Code Available)
MPR: A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration by Bartolomeo Della Corte, Igor Bogoslavskyi, Cyrill Stachniss, Giorgio Grisetti
ICRA 2018
PDF: https://arxiv.org/abs/1709.05945
Code:https://gitlab.com/srrg-software/srrg_mpr
Abstract:
The ability to build maps is a key functionality for the majority of mobile robots. A central ingredient to most mapping systems is the registration or alignment of the recorded sensor data. In this paper, we present a general methodology for photometric registration that can deal with multiple different cues. We provide examples for registering RGBD as well as 3D LIDAR data. In contrast to popular point cloud registration approaches such as ICP our method does not rely on explicit data association and exploits multiple modalities such as raw range and image data streams. Color, depth, and normal information are handled in an uniform manner and the registration is obtained by minimizing the pixel-wise difference between two multi-channel images. We developed a flexible and general framework and implemented our approach inside that framework. We also released our implementation as open source C++ code. The experiments show that our approach allows for an accurate registration of the sensor data without requiring an explicit data association or model-specific adaptations to datasets or sensors. Our approach exploits the different cues in a natural and consistent way and the registration can be done at framerate for a typical range or imaging sensor.
Multi-Trajectory Visual Place Recognition in Changing Environments
Multi-Trajectory Visual Place Recognition in Changing Environments by Olga Vysotska and Cyrill Stachniss. See RAL’2019
RSS’2018: Efficient Surfel-based Mapping using 3D Laser Range Data
J. Behley and C. Stachniss, “Efficient Surfel-based SLAM using 3D Laser Range Data in Urban Environments,” in Proceedings of Robotics: Science and Systems (RSS), 2018.
PFG’2017/IROS’2016: Efficient Online Segmentation for Sparse 3D Laser Scans
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.
as well as
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.
Spitzenforschung: Uni Bonn stellt acht Anträge für Exzellenzcluster
Mit Exzellenzclustern möchten Bund und Länder die Spitzenforschung in Deutschland antreiben. Es geht um insgesamt rund 385 Millionen Euro mit denen zwischen 45 und 50 Projekte gefördert werden sollen. Welche die Uni Bonn ins Rennen geschickt hat, das sehen Sie im Video.
IROS’2016: High-Speed Segmentation of 3D Range Scans
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.
Online sugar beets vs. weed classification on the field (FLOURISH Project)
Related Paper:
P. Lottes, M. Hoeferlin, S. Sanders, and C. Stachniss: “Effective Vision-Based Classification for Separating Sugar Beets and Weeds for Precision Farming”, Journal of Field Robotics, 2016
Lazy Data Association For Image Sequences Matching Under Substantial Appearance Changes
Olga Vysotska and Cyrill Stachniss
Lazy Data Association For Image Sequences Matching Under Substantial Appearance Changes
IEEE Robotics and Automation Letters (RA-L) and presentation at ICRA 2016, 2016
ISPRS Congress Video – DAY 5
Watch the report from the XXIII ISPRS Congress – Day 5
ROVINA Project – Homing Example
Homing example performed in the Priscilla Catacomb in Rome
ROVINA Project: Data Acquisition for Visual Mapping of the Catacombe di Priscilla
Graph-Based SLAM using Building Information from Open Street Maps, 2016
Graph-Based SLAM using Building Information from Open Street Maps by Olga Vysotska and Cyrill Stachniss, 2016
Visual Across Season Matching Exploiting Location Priors
O. Vysotska, T. Naseer, L. Spinello, W. Burgard, C. Stachniss:
“Efficient and Effective Matching of Image Sequences Under Substantial Appearance Changes Exploiting GPS Priors”,
In Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA)., pp. 2774-2779. 2015.
Collaborative Filtering for Predicting User Preferences For Organizing Objects, 2014/15
by N. Abdo, C. Stachniss, L. Spinello, and W. Burgard
2014/15
MoD Project: Mapping using a monocular camera and the PO Box for georeferencing
MoD Project: Direct georeferencing using PO Box and visual odometry
Rovina Project – Driving up and down the stairs
Rovina Project – Climbing stairs with the new platform
Robot: Modified MESA Element platform by built Algorithmica.IT within the Rovina Project (FP7-ICT-600890)
EUROPA Project: 30s trailer from the downtown navigation event, 2012
A 30s video showing parts of the public downtown navigation experiments of the European Robotic Pedestrian Assistant Obelix.
EUROPA Project – Public final demo, 2012
Public final demo of the EUROPA project, August 2012
EUROPA Project – Test run for the final demo, 2012
Test run for the final demo of the EUROPA project in summer 2012
EUROPA Project – Online mapping and exploration, 2011
Results of the year 3 developed during the EUROPA project funded by the EC in FP7.
EUROPA Project – Autonous navigation from the Freiburg campus to the hospital
Results of the year 2 developed during the EUROPA project funded by the EC in FP7.
The robot autonomously navigates from the Freiburg campus to the University hospital
EUROPA Project – Traversability analysis, 2010
Results of the year 2 developed during the EUROPA project funded by the EC in FP7.
The videos shows the traversability and vegetation analysis of the robot:
yellow=traversable
red=obstacle
green=vegetation
Robust map optimization with outlier constraints – DCS vs. SC, 2012-2013
Pratik Agarwal, Gian Diego Tipaldi, Luciano Spinello, Cyrill Stachniss, and Wolfram Burgard. Robust Map Optimization Using Dynamic Covariance Scaling.
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 2013.
Completing a table scene based on previously observed scenes, 2011-2012
Dominik Joho, Diego Tipald, Nickolas Engelhard, Cyrill Stachniss, and Wolfram Burgard
Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction.
In Proc. of Robotics: Science and Systems (RSS), 2012.
PR2 keeping a door open based on actions learnt from demonstration, 2012
Nichola Abdo, Henrik Kretzschmar, Luciano Spinello, and Cyrill Stachniss
Learning Manipulation Actions from a Few Demonstrations.
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 2013.
Nichola Abdo, Henrik Kertzschmar, and Cyrill Stachniss
From Low-Level Trajectory Demonstrations to Symbolic Actions for Planning.
In Proc. of the ICAPS Workshop on Combining Task and Motion Planning for Real-World Applications (TAMPRA), 2012.
PR2 solving a planning problem using actions it learnt before by observing a human, 2012
Nichola Abdo, Henrik Kretzschmar, Luciano Spinello, and Cyrill Stachniss
Learning Manipulation Actions from a Few Demonstrations.
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 2013.
Nichola Abdo, Henrik Kertzschmar, and Cyrill Stachniss
From Low-Level Trajectory Demonstrations to Symbolic Actions for Planning.
In Proc. of the ICAPS Workshop on Combining Task and Motion Planning for Real-World Applications (TAMPRA), 2012.
Fine localization and positioning with the KUKA OmniRob – Example, 2012
Joerg Roewekaamper, Christoph Sprunk, Gian Diego Tipaldi, Cyrill Stachniss, Patrick Pfaff, and Wolfram Burgard
On the Position Accuracy of Mobile Robot Localization based on Particle Filters combined with Scan Matching.
In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2012.
Fine localization and positioning with the KUKA OmniRob – Evaluation experiments, 2012
Joerg Roewekaamper, Christoph Sprunk, Gian Diego Tipaldi, Cyrill Stachniss, Patrick Pfaff, and Wolfram Burgard
On the Position Accuracy of Mobile Robot Localization based on Particle Filters combined with Scan Matching.
In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2012.
SLAM – Graph-based SLAM with Node Reduction – Intel, 2011
Henrik Kretzschmar and Cyrill Stachniss
Information-Theoretic Compression of Pose Graphs for Laser-Based SLAM.
International Journal on Robotics Research (IJRR), Volume 31(11), 2012.
Cyrill Stachniss and Henrik Kretzschmar
Pose Graph Compression for Laser-based SLAM.
In Proc. of the Int. Symposium of Robotics Research (ISRR),
Flagstaff, AZ, USA, 2011. Invited presentation.
SLAM – Graph-based SLAM – Intel, 2011
Used as the base-line in:
Henrik Kretzschmar and Cyrill Stachniss
Information-Theoretic Compression of Pose Graphs for Laser-Based SLAM.
International Journal on Robotics Research (IJRR), Volume 31(11), 2012.
Cyrill Stachniss and Henrik Kretzschmar
Pose Graph Compression for Laser-based SLAM.
In Proc. of the Int. Symposium of Robotics Research (ISRR),
Flagstaff, AZ, USA, 2011. Invited presentation.
SLAM – Graph-based SLAM – Freiburg79, 2011
Used as the base-line in:
Henrik Kretzschmar and Cyrill Stachniss
Information-Theoretic Compression of Pose Graphs for Laser-Based SLAM.
International Journal on Robotics Research (IJRR), Volume 31(11), 2012.
Cyrill Stachniss and Henrik Kretzschmar
Pose Graph Compression for Laser-based SLAM.
In Proc. of the Int. Symposium of Robotics Research (ISRR),
Flagstaff, AZ, USA, 2011. Invited presentation.
SLAM – Graph-based SLAM with Node Reduction – Freiburg79, 2011
Henrik Kretzschmar and Cyrill Stachniss
Information-Theoretic Compression of Pose Graphs for Laser-Based SLAM.
International Journal on Robotics Research (IJRR), Volume 31(11), 2012.
Cyrill Stachniss and Henrik Kretzschmar
Pose Graph Compression for Laser-based SLAM.
In Proc. of the Int. Symposium of Robotics Research (ISRR),
Flagstaff, AZ, USA, 2011. Invited presentation.
Learning kinematic models of articulated objects – Dishwasher door, 2009-2011
Juergen Sturm, Cyrill Stachniss, and Wolfram Burgard
A probabilistic framework for learning kinematic models of articulated objects.
Journal on Artificial Intelligence Research (JAIR), Volume 41, pages 477-526, 2011.
SLAM – Graph-based SLAM – FHW, 2011
Used as the base-line in:
Henrik Kretzschmar and Cyrill Stachniss
Information-Theoretic Compression of Pose Graphs for Laser-Based SLAM.
International Journal on Robotics Research (IJRR), Volume 31(11), 2012.
Cyrill Stachniss and Henrik Kretzschmar
Pose Graph Compression for Laser-based SLAM.
In Proc. of the Int. Symposium of Robotics Research (ISRR),
Flagstaff, AZ, USA, 2011. Invited presentation.
Octree maps, 2010-2013
Kai Wurm, Armin Hornung, Maren Bennewitz, Cyrill Stachniss, and Wolfram 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, Alaska, 2010.
Armin Hornung, Kai M. Wurm, Maren Bennewitz, Cyrill Stachniss, and Wolfram Burgard
OctoMap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees.
Autonomous Robots, 2013
SLAM – HOG-Man – Two levels of the pose-graph – Stanford garage, 2011
Giorgio Grisetti, Rainer Kümmerle, Cyrill Stachniss, Udo Frese, and Christoph Hertzberg
Hierarchical Optimization on Manifolds for Online 2D and 3D Mapping.
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Anchorage, Alaska, 2010.
SLAM – HOG-Man – Two levels of the pose-graph – Intel , 2011
Giorgio Grisetti, Rainer Kümmerle, Cyrill Stachniss, Udo Frese, and Christoph Hertzberg
Hierarchical Optimization on Manifolds for Online 2D and 3D Mapping.
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Anchorage, Alaska, 2010.
Vision-Based Humanoid Navigation Using Self-Supervised Obstacle Detection, 2011-2013
Daniel Maier, Cyrill Stachniss, and Maren Bennewitz
Vision-Based Humanoid Navigation Using Self-Supervised Obstacle Detection.
International Journal of Humanoid Robotics, 2013.
Daniel Maier, Maren Bennewitz, and Cyrill Stachniss
Self-supervised Obstacle Detection for Humanoid Navigation Using Monocular Vision and Sparse Laser Data.
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 2011.
Learning kinematic models of articulated objects – Two drawers, 2009-2011
Juergen Sturm, Cyrill Stachniss, and Wolfram Burgard
A probabilistic framework for learning kinematic models of articulated objects.
Journal on Artificial Intelligence Research (JAIR), Volume 41, pages 477-526, 2011.
Learning kinematic models of articulated objects – Dishwasher tray, 2009-2011
Juergen Sturm, Cyrill Stachniss, and Wolfram Burgard
A probabilistic framework for learning kinematic models of articulated objects.
Journal on Artificial Intelligence Research (JAIR), Volume 41, pages 477-526, 2011.
Learning kinematic models of articulated objects – Fridge, 2009-2011
Juergen Sturm, Cyrill Stachniss, and Wolfram Burgard
A probabilistic framework for learning kinematic models of articulated objects.
Journal on Artificial Intelligence Research (JAIR), Volume 41, pages 477-526, 2011.
Learning kinematic models of articulated objects – Heating, 2009-2011
Juergen Sturm, Cyrill Stachniss, and Wolfram Burgard
A probabilistic framework for learning kinematic models of articulated objects.
Journal on Artificial Intelligence Research (JAIR), Volume 41, pages 477-526, 2011.
Learning kinematic models of articulated objects – Water tap, 2009-2011
Juergen Sturm, Cyrill Stachniss, and Wolfram Burgard
A probabilistic framework for learning kinematic models of articulated objects.
Journal on Artificial Intelligence Research (JAIR), Volume 41, pages 477-526, 2011.
Learning kinematic models of articulated objects – Door, 2009-2011
Juergen Sturm, Cyrill Stachniss, and Wolfram Burgard
A probabilistic framework for learning kinematic models of articulated objects.
Journal on Artificial Intelligence Research (JAIR), Volume 41, pages 477-526, 2011.
Learning kinematic models of articulated objects – Drawer, 2009-2011
Juergen Sturm, Cyrill Stachniss, and Wolfram Burgard
A probabilistic framework for learning kinematic models of articulated objects.
Journal on Artificial Intelligence Research (JAIR), Volume 41, pages 477-526, 2011.
Navigation with online vegetation detection, 2009-2013
Kai Wurm, Henrik Kretzschmar, Rainer Kuemmerle, Cyrill Stachniss, and Wolfram Burgard
Identifying Vegetation from Laser Data in Structured Outdoor Environments.
Robotics and Autonomous Systems, 2013
Kai M. Wurm, Rainer Kuemmerle, Cyrill Stachniss, and Wolfram Burgard
Improving Robot Navigation in Structured Outdoor Environments by Identifying Vegetation from Laser Data.
In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2009.
Planning in environments with deformable objects – Curtain – 2008-2010
Barbara Frank, Cyrill Stachniss, Ruediger Schmedding, Wolfram Burgard, Matthias Teschner
Real-world Robot Navigation amongst Deformable Obstacles.
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, 2009.
Barbara Frank, Markus Becker, Cyrill Stachniss, Matthias Teschner, and Wolfram Burgard
Learning Cost Functions for Mobile Robot Navigation in Environments with Deformable Objects.
Workshop on Path Planning on Cost Maps at the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA, USA, 2008.
Planning in environments with deformable objects – Ducks – 2008-2010
Barbara Frank, Cyrill Stachniss, Ruediger Schmedding, Wolfram Burgard, Matthias Teschner
Real-world Robot Navigation amongst Deformable Obstacles.
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, 2009.
Barbara Frank, Markus Becker, Cyrill Stachniss, Matthias Teschner, and Wolfram Burgard
Learning Cost Functions for Mobile Robot Navigation in Environments with Deformable Objects.
Workshop on Path Planning on Cost Maps at the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA, USA, 2008.
Planning in environments with deformable objects – Cow – 2008-2010
Barbara Frank, Cyrill Stachniss, Ruediger Schmedding, Wolfram Burgard, Matthias Teschner
Real-world Robot Navigation amongst Deformable Obstacles.
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, 2009.
Barbara Frank, Markus Becker, Cyrill Stachniss, Matthias Teschner, and Wolfram Burgard
Learning Cost Functions for Mobile Robot Navigation in Environments with Deformable Objects.
Workshop on Path Planning on Cost Maps at the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA, USA, 2008.
SLAM – Graph-based SLAM with Gauss-Newton Optimization – FR Campus, 2009-2010
Giorgio Grisetti, Rainer Kuemmerle, Cyrill Stachniss, and Wolfram Burgard
A Tutorial on Graph-based SLAM.
IEEE Transactions on Intelligent Transportation Systems Magazine. Volume 2(4), pages 31–43, 2010.
Blimp navigation with downlooking camera and online SLAM with TORO, 2007
Batian Steder, Axel Rottmann, Giorgio Grisetti, Cyrill Stachniss, and Wolfram Burgard
Autonomous Navigation for Small Flying Vehicles.
In Workshop on Micro Aerial Vehicles at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2007.
SLAM – TORO – Sphere Optimization, 2007-2009
Giorgio Grisetti, Cyrill Stachniss, and Wolfram Burgard
Non-linear Constraint Network Optimization for Efficient Map Learning.
IEEE Transactions on Intelligent Transportation Systems, Volume 10, Issue 3, Pages 428-439, 2009.
Giorgio Grisetti, Cyrill Stachniss, Slawomir Grzonka, and Wolfram Burgard
A Tree Parameterization for Efficiently Computing Maximum Likelihood Maps using Gradient Descent.
Robotics: Science and Systems (RSS), Atlanta, GA, USA, 2007.
Multi-Robot Exploration – Simulation, 2008
Kai M. Wurm, Cyrill Stachniss, and Wolfram Burgard
Coordinated Multi-Robot Exploration using a Segmentation of the Environment.
In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2008.
Multi-Robot Exploration – Freiburg79, 2008
Kai M. Wurm, Cyrill Stachniss, and Wolfram Burgard
Coordinated Multi-Robot Exploration using a Segmentation of the Environment.
In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2008.
Carrera Racing – Preparing a student project on autonomous slot car racing, 2008
First tests with our Carrera racing track and an onboard camera in Spring 2008
Autonomous Smart car at EPFL, 2006
Autonomous Smart car developed by EPFL, ETH Zurich and University of Freiburg.
Autonomous Smart car at EPF – with taffic , 2006
Autonomous Smart car developed by EPFL, ETH Zurich and University of Freiburg.
Information-Driven Exploration, 2005
Cyrill Stachniss, Giorgio Grisetti, and Wolfram Burgard
Information Gain-based Exploration Using Rao-Blackwellized Particle Filters.
In Proc. of Robotics: Science and Systems (RSS), pages 65-72, Cambridge, MA, USA, 2005.
SLAM – GMapping – MIT – 2005-2007
Giorgio Grisetti, Cyrill Stachniss, and Wolfram Burgard
Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters.
Transactions on Robotics, Volume 23, pages 34-46, 2007
Cyrill Stachniss, Grisetti Giorgio, Wolfram Burgard, and Nicholas Roy
Analyzing Gaussian Proposal Distributions for Mapping with Rao-Blackwellized Particle Filters.
In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2007.
SLAM – GridFastSLAM – Voronoi, 2006
Combines GMapping and Voronoi diagram computation for the best sample
by Cyrill Stachniss
Semantic labeling of places, 2005
Axel Rottmann, Oscar Martinez Mozos, Cyrill Stachniss, and Wolfram Burgard
Semantic Place Classification of Indoor Environments with Mobile Robots using Boosting.
In Proc. of the National Conference on Artificial Intelligence (AAAI),
pages 1306-1311, Pittsburgh, PA, USA, 2005.