Research Videos

IROS 2020: F. Magistri: Segmentation-Based 4D Registration of Plants Point Clouds ... (Trailer)

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)

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)

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)

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!

DIGICROP'2020: Spatio-Temporal Registration of Plant Point Clouds by Chebrolu et al. (Trailer)

DIGICROP’2020: Spatio-Temporal Registration of Plant Point Clouds by Chebrolu et al. (Trailer)

RSS'2020: OverlapNet - Loop Closing for LiDAR-based SLAM by X. Chen et al.

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

ICRA'2020: Visual Servoing-based Navigation for Monitoring Crop Row Fields by Ahmadi et al.

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

IROS'19: ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras...

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.

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

ICRA'2019: Actively Improving Robot Navigation On Different Terrains Using GPMMs

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: Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs

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

ICRA'2019: Robot Localization Based on Aerial Images for Precision Agriculture

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

IROS'2018 Workshop: Towards Uncertainty-Aware Path Planning for Navigation

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

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

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.

Bonnet: Prediction of Cityscapes

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

IROS'2018: Joint Ego-motion Estimation Using a Laser Scanner and a Monocular Camera

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

RSS'2018: Efficient Surfel-based Mapping using 3D Laser Range Data

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.

ICRA'2018: Fast Image-Based Geometric Change Detection Given a 3D Model

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

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

ICRA'2018: Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots ...

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. https://arxiv.org/abs/1709.06764

ICRA'2018: A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration

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. Code: https://gitlab.com/srrg-software/srrg_mpr

ICRA'18: Flexible Multi-Cue Photometric Point Cloud Registration (Code Available)

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.

PFG'2017/IROS'2016: Efficient Online Segmentation for Sparse 3D Laser Scans

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.

Graph-Based SLAM using Building Information from Open Street Maps, 2016

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

IROS'2016: High-Speed Segmentation of 3D Range Scans

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.

Visual Across Season Matching Exploiting Location Priors

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

Collaborative Filtering for Predicting User Preferences For Organizing Objects, 2014/15

by N. Abdo, C. Stachniss, L. Spinello, and W. Burgard 2014/15