Dr. Jens Behley

Postdoctoral researcher
Contact:
Email: jens.behley@nulligg.uni-bonn.de
Tel: +49 – 228 – 73 – 60 190
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.008
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Research Interests

  • Laser-based perception
  • Machine learning for robotic applications

Short CV

Jens Behley is a postdoctoral researcher at the Department for Photogrammetry since February 2016. From September 2008 to July 2015, Jens worked at the Department for Computer Science III, University of Bonn and he successfully defended his PhD thesis on “Three-dimensional Laser-based Classification in Outdoor Environments” supervised by Prof. Dr. Armin B. Cremers in January 2014.

Awards

  • Finalist for Best System Paper at Robotics: Science and Systems (RSS) 2020
  • Diplomarbeitspreis der Bonner Informatik Gesellschaft e.V. (2009)

Publications

2021

  • B. Mersch, X. Chen, J. Behley, and C. Stachniss, “Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks,” in Proc. of the Conf. on Robot Learning (CoRL), 2021.
    [BibTeX] [PDF] [Code] [Video]
    @InProceedings{mersch2021corl,
    author = {B. Mersch and X. Chen and J. Behley and C. Stachniss},
    title = {{Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks}},
    booktitle = corl,
    year = {2021},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/mersch2021corl.pdf},
    codeurl = {https://github.com/PRBonn/point-cloud-prediction},
    videourl = {https://youtu.be/-pSZpPgFAso},
    }
  • J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, J. Gall, and C. Stachniss, “Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset,” The Intl. Journal of Robotics Research, vol. 40, iss. 8-9, pp. 959-967, 2021. doi:10.1177/02783649211006735
    [BibTeX] [PDF]
    @article{behley2021ijrr,
    author = {J. Behley and M. Garbade and A. Milioto and J. Quenzel and S. Behnke and J. Gall and C. Stachniss},
    title = {Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset},
    journal = ijrr,
    volume = {40},
    number = {8-9},
    pages = {959-967},
    year = {2021},
    doi = {10.1177/02783649211006735},
    url = {http://www.ipb.uni-bonn.de/pdfs/behley2021ijrr.pdf}
    }
  • X. Chen, T. Läbe, A. Milioto, T. Röhling, J. Behley, and C. Stachniss, “OverlapNet: A Siamese Network for Computing LiDAR Scan Similarity with Applications to Loop Closing and Localization,” Autonomous Robots, 2021. doi:10.1007/s10514-021-09999-0
    [BibTeX] [PDF] [Code]
    @article{chen2021auro,
    author = {X. Chen and T. L\"abe and A. Milioto and T. R\"ohling and J. Behley and C. Stachniss},
    title = {{OverlapNet: A Siamese Network for Computing LiDAR Scan Similarity with Applications to Loop Closing and Localization}},
    journal = auro,
    year = {2021},
    doi = {10.1007/s10514-021-09999-0},
    issn = {1573-7527},
    codeurl = {https://github.com/PRBonn/OverlapNet/},
    url = {http://www.ipb.uni-bonn.de/pdfs/chen2021auro.pdf}
    }
  • P. Rottmann, T. Posewsky, A. Milioto, C. Stachniss, and J. Behley, “Improving Monocular Depth Estimation by Semantic Pre-training,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2021.
    [BibTeX] [PDF]
    @inproceedings{rottmann2021iros,
    title = {{Improving Monocular Depth Estimation by Semantic Pre-training}},
    author = {P. Rottmann and T. Posewsky and A. Milioto and C. Stachniss and J. Behley},
    booktitle = iros,
    year = {2021},
    url = {http://www.ipb.uni-bonn.de/pdfs/rottmann2021iros.pdf}
    }
  • X. Chen, S. Li, B. Mersch, L. Wiesmann, J. Gall, J. Behley, and C. Stachniss, “Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data,” IEEE Robotics and Automation Letters (RA-L), vol. 6, pp. 6529-6536, 2021. doi:10.1109/LRA.2021.3093567
    [BibTeX] [PDF] [Code] [Video]
    @article{chen2021ral,
    title={{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data}},
    author={X. Chen and S. Li and B. Mersch and L. Wiesmann and J. Gall and J. Behley and C. Stachniss},
    year={2021},
    volume=6,
    issue=4,
    pages={6529-6536},
    journal=ral,
    url = {http://www.ipb.uni-bonn.de/pdfs/chen2021ral-iros.pdf},
    codeurl = {https://github.com/PRBonn/LiDAR-MOS},
    videourl = {https://youtu.be/NHvsYhk4dhw},
    doi = {10.1109/LRA.2021.3093567},
    issn = {2377-3766},
    }
  • M. Aygün, A. Osep, M. Weber, M. Maximov, C. Stachniss, J. Behley, and L. Leal-Taixe, “4D Panoptic Segmentation,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.
    [BibTeX] [PDF]
    @inproceedings{ayguen2021cvpr,
    author = {M. Ayg\"un and A. Osep and M. Weber and M. Maximov and C. Stachniss and J. Behley and L. Leal-Taixe},
    title = {{4D Panoptic Segmentation}},
    booktitle = cvpr,
    year = 2021,
    url = {http://www.ipb.uni-bonn.de/pdfs/ayguen2021cvpr.pdf}
    }
  • F. Magistri, N. Chebrolu, J. Behley, and C. Stachniss, “Towards In-Field Phenotyping Exploiting Differentiable Rendering with Self-Consistency Loss,” in Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2021.
    [BibTeX] [PDF] [Video]
    @inproceedings{magistri2021icra,
    author = {F. Magistri and N. Chebrolu and J. Behley and C. Stachniss},
    title = {{Towards In-Field Phenotyping Exploiting Differentiable Rendering with Self-Consistency Loss}},
    booktitle = icra,
    year = 2021,
    videourl = {https://youtu.be/MF2A4ihY2lE},
    }
  • I. Vizzo, X. Chen, N. Chebrolu, J. Behley, and C. Stachniss, “Poisson Surface Reconstruction for LiDAR Odometry and Mapping,” in Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2021.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{vizzo2021icra,
    author = {I. Vizzo and X. Chen and N. Chebrolu and J. Behley and C. Stachniss},
    title = {{Poisson Surface Reconstruction for LiDAR Odometry and Mapping}},
    booktitle = icra,
    year = 2021,
    url = {http://www.ipb.uni-bonn.de/pdfs/vizzo2021icra.pdf},
    codeurl = {https://github.com/PRBonn/puma},
    videourl = {https://youtu.be/7yWtYWaO5Nk}
    }
  • X. Chen, I. Vizzo, T. Läbe, J. Behley, and C. Stachniss, “Range Image-based LiDAR Localization for Autonomous Vehicles,” in Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2021.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{chen2021icra,
    author = {X. Chen and I. Vizzo and T. L{\"a}be and J. Behley and C. Stachniss},
    title = {{Range Image-based LiDAR Localization for Autonomous Vehicles}},
    booktitle = icra,
    year = 2021,
    url = {http://www.ipb.uni-bonn.de/pdfs/chen2021icra.pdf},
    codeurl = {https://github.com/PRBonn/range-mcl},
    videourl = {https://youtu.be/hpOPXX9oPqI},
    }
  • J. Behley, A. Milioto, and C. Stachniss, “A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI,” in Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2021.
    [BibTeX] [PDF]
    @inproceedings{behley2021icra,
    author = {J. Behley and A. Milioto and C. Stachniss},
    title = {{A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI}},
    booktitle = icra,
    year = 2021,
    }
  • N. Chebrolu, T. Läbe, O. Vysotska, J. Behley, and C. Stachniss, “Adaptive Robust Kernels for Non-Linear Least Squares Problems,” IEEE Robotics and Automation Letters (RA-L), vol. 6, pp. 2240-2247, 2021. doi:10.1109/LRA.2021.3061331
    [BibTeX] [PDF] [Video]
    @article{chebrolu2021ral,
    author = {N. Chebrolu and T. L\"{a}be and O. Vysotska and J. Behley and C. Stachniss},
    title = {{Adaptive Robust Kernels for Non-Linear Least Squares Problems}},
    journal = ral,
    volume = 6,
    issue = 2,
    pages = {2240-2247},
    doi = {10.1109/LRA.2021.3061331},
    year = 2021,
    videourl = {https://youtu.be/34Zp3ZX0Bnk}
    }
  • J. Weyler, A. Milioto, T. Falck, J. Behley, and C. Stachniss, “Joint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping,” IEEE Robotics and Automation Letters (RA-L), vol. 6, pp. 3599-3606, 2021. doi:10.1109/LRA.2021.3060712
    [BibTeX] [PDF] [Video]
    @article{weyler2021ral,
    author = {J. Weyler and A. Milioto and T. Falck and J. Behley and C. Stachniss},
    title = {{Joint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping}},
    journal = ral,
    volume = 6,
    issue = 2,
    pages = {3599-3606},
    doi = {10.1109/LRA.2021.3060712},
    year = 2021,
    videourl = {https://youtu.be/Is18Rey625I},
    }
  • L. Wiesmann, A. Milioto, X. Chen, C. Stachniss, and J. Behley, “Deep Compression for Dense Point Cloud Maps,” IEEE Robotics and Automation Letters (RA-L), vol. 6, pp. 2060-2067, 2021. doi:10.1109/LRA.2021.3059633
    [BibTeX] [PDF] [Code] [Video]
    @article{wiesmann2021ral,
    author = {L. Wiesmann and A. Milioto and X. Chen and C. Stachniss and J. Behley},
    title = {{Deep Compression for Dense Point Cloud Maps}},
    journal = ral,
    volume = 6,
    issue = 2,
    pages = {2060-2067},
    doi = {10.1109/LRA.2021.3059633},
    year = 2021,
    url = {http://www.ipb.uni-bonn.de/pdfs/wiesmann2021ral.pdf},
    codeurl = {https://github.com/PRBonn/deep-point-map-compression},
    videourl = {https://youtu.be/fLl9lTlZrI0}
    }

2020

  • A. Milioto, J. Behley, C. McCool, and C. Stachniss, “LiDAR Panoptic Segmentation for Autonomous Driving,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020.
    [BibTeX] [PDF] [Video]
    @inproceedings{milioto2020iros,
    author = {A. Milioto and J. Behley and C. McCool and C. Stachniss},
    title = {{LiDAR Panoptic Segmentation for Autonomous Driving}},
    booktitle = iros,
    year = {2020},
    videourl = {https://www.youtube.com/watch?v=C9CTQSosr9I},
    }
  • X. Chen, T. Läbe, L. Nardi, J. Behley, and C. Stachniss, “Learning an Overlap-based Observation Model for 3D LiDAR Localization,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{chen2020iros,
    author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
    title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
    booktitle = iros,
    year = {2020},
    codeurl = {https://github.com/PRBonn/overlap_localization},
    url={http://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
    videourl = {https://www.youtube.com/watch?v=BozPqy_6YcE},
    }
  • F. Langer, A. Milioto, A. Haag, J. Behley, and C. Stachniss, “Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{langer2020iros,
    author = {F. Langer and A. Milioto and A. Haag and J. Behley and C. Stachniss},
    title = {{Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks}},
    booktitle = iros,
    year = {2020},
    url = {http://www.ipb.uni-bonn.de/pdfs/langer2020iros.pdf},
    videourl = {https://youtu.be/6FNGF4hKBD0},
    codeurl = {https://github.com/PRBonn/lidar_transfer},
    }
  • 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 Proc. of Robotics: Science and Systems (RSS), 2020.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{chen2020rss,
    author = {X. Chen and T. L\"abe and A. Milioto and T. R\"ohling and O. Vysotska and A. Haag and J. Behley and C. Stachniss},
    title = {{OverlapNet: Loop Closing for LiDAR-based SLAM}},
    booktitle = {Proc. of Robotics: Science and Systems (RSS)},
    year = {2020},
    codeurl = {https://github.com/PRBonn/OverlapNet/},
    videourl = {https://youtu.be/YTfliBco6aw},
    }
  • N. Chebrolu, T. Laebe, O. Vysotska, J. Behley, and C. Stachniss, “Adaptive Robust Kernels for Non-Linear Least Squares Problems,” arXiv Preprint, 2020.
    [BibTeX] [PDF]
    @article{chebrolu2020arxiv,
    title={Adaptive Robust Kernels for Non-Linear Least Squares Problems},
    author={N. Chebrolu and T. Laebe and O. Vysotska and J. Behley and C. Stachniss},
    journal = arxiv,
    year=2020,
    eprint={2004.14938},
    keywords={cs.RO},
    url={https://arxiv.org/pdf/2004.14938v2}
    }
  • J. Behley, A. Milioto, and C. Stachniss, “A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI,” arXiv Preprint, 2020.
    [BibTeX] [PDF]
    Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly. In this paper, we present an extension of SemanticKITTI, which is a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Benchmark, for training and evaluation of laser-based panoptic segmentation. We provide the data and discuss the processing steps needed to enrich a given semantic annotation with temporally consistent instance information, i.e., instance information that supplements the semantic labels and identifies the same instance over sequences of LiDAR point clouds. Additionally, we present two strong baselines that combine state-of-the-art LiDAR-based semantic segmentation approaches with a state-of-the-art detector enriching the segmentation with instance information and that allow other researchers to compare their approaches against. We hope that our extension of SemanticKITTI with strong baselines enables the creation of novel algorithms for LiDAR-based panoptic segmentation as much as it has for the original semantic segmentation and semantic scene completion tasks. Data, code, and an online evaluation using a hidden test set will be published on http://semantic-kitti.org.
    @article{behley2020arxiv,
    author = {J. Behley and A. Milioto and C. Stachniss},
    title = {{A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI}},
    journal = arxiv,
    year = 2020,
    eprint = {2003.02371v1},
    url = {http://arxiv.org/pdf/2003.02371v1},
    keywords = {cs.CV},
    abstract = {Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly. In this paper, we present an extension of SemanticKITTI, which is a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Benchmark, for training and evaluation of laser-based panoptic segmentation. We provide the data and discuss the processing steps needed to enrich a given semantic annotation with temporally consistent instance information, i.e., instance information that supplements the semantic labels and identifies the same instance over sequences of LiDAR point clouds. Additionally, we present two strong baselines that combine state-of-the-art LiDAR-based semantic segmentation approaches with a state-of-the-art detector enriching the segmentation with instance information and that allow other researchers to compare their approaches against. We hope that our extension of SemanticKITTI with strong baselines enables the creation of novel algorithms for LiDAR-based panoptic segmentation as much as it has for the original semantic segmentation and semantic scene completion tasks. Data, code, and an online evaluation using a hidden test set will be published on http://semantic-kitti.org.}
    }
  • P. Lottes, J. Behley, N. Chebrolu, A. Milioto, and C. Stachniss, “Robust joint stem detection and crop-weed classification using image sequences for plant-specific treatment in precision farming,” Journal of Field Robotics, vol. 37, pp. 20-34, 2020. doi:https://doi.org/10.1002/rob.21901
    [BibTeX] [PDF]
    @Article{lottes2020jfr,
    title = {Robust joint stem detection and crop-weed classification using image sequences for plant-specific treatment in precision farming},
    author = {Lottes, P. and Behley, J. and Chebrolu, N. and Milioto, A. and Stachniss, C.},
    journal = jfr,
    volume = {37},
    numer = {1},
    pages = {20-34},
    year = {2020},
    doi = {https://doi.org/10.1002/rob.21901},
    url = {http://www.ipb.uni-bonn.de/pdfs/lottes2019jfr.pdf},
    }

2019

  • 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.
    [BibTeX] [PDF] [Video]
    @InProceedings{behley2019iccv,
    author = {J. Behley and M. Garbade and A. Milioto and J. Quenzel and S. Behnke and C. Stachniss and J. Gall},
    title = {{SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences}},
    booktitle = iccv,
    year = {2019},
    videourl = {http://www.ipb.uni-bonn.de/html/projects/semantic_kitti/videos/teaser.mp4},
    }
  • E. Palazzolo, J. Behley, P. Lottes, P. Giguère, and C. Stachniss, “ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2019.
    [BibTeX] [PDF] [Code] [Video]
    @InProceedings{palazzolo2019iros,
    author = {E. Palazzolo and J. Behley and P. Lottes and P. Gigu\`ere and C. Stachniss},
    title = {{ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals}},
    booktitle = iros,
    year = {2019},
    url = {http://www.ipb.uni-bonn.de/pdfs/palazzolo2019iros.pdf},
    codeurl = {https://github.com/PRBonn/refusion},
    videourl = {https://youtu.be/1P9ZfIS5-p4},
    }
  • X. Chen, A. Milioto, E. Palazzolo, P. Giguère, J. Behley, and C. Stachniss, “SuMa++: Efficient LiDAR-based Semantic SLAM,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2019.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{chen2019iros,
    author = {X. Chen and A. Milioto and E. Palazzolo and P. Giguère and J. Behley and C. Stachniss},
    title = {{SuMa++: Efficient LiDAR-based Semantic SLAM}},
    booktitle = {Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = 2019,
    codeurl = {https://github.com/PRBonn/semantic_suma/},
    videourl = {https://youtu.be/uo3ZuLuFAzk},
    }
  • A. Milioto, I. Vizzo, J. Behley, and C. Stachniss, “RangeNet++: Fast and Accurate LiDAR Semantic Segmentation,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2019.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{milioto2019iros,
    author = {A. Milioto and I. Vizzo and J. Behley and C. Stachniss},
    title = {{RangeNet++: Fast and Accurate LiDAR Semantic Segmentation}},
    booktitle = {Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = 2019,
    codeurl = {https://github.com/PRBonn/lidar-bonnetal},
    videourl = {https://youtu.be/wuokg7MFZyU},
    }

2018

  • P. Lottes, J. Behley, A. Milioto, and C. Stachniss, “Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming,” IEEE Robotics and Automation Letters (RA-L), vol. 3, pp. 3097-3104, 2018. doi:10.1109/LRA.2018.2846289
    [BibTeX] [PDF] [Video]
    @Article{lottes2018ral,
    author = {P. Lottes and J. Behley and A. Milioto and C. Stachniss},
    title = {Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming},
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    year = {2018},
    volume = {3},
    issue = {4},
    pages = {3097-3104},
    doi = {10.1109/LRA.2018.2846289},
    url = {http://www.ipb.uni-bonn.de/pdfs/lottes2018ral.pdf},
    videourl = {https://www.youtube.com/watch?v=vTepw9HRLh8},
    }
  • P. Lottes, J. Behley, N. Chebrolu, A. Milioto, and C. Stachniss, “Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2018.
    [BibTeX] [PDF] [Video]
    Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need the ability to identify crops and weeds in the field and must additionally select effective treatments. While certain types of weed can be treated mechanically, other types need to be treated by (selective) spraying. In this paper, we present an approach that provides the necessary information for effective plant-specific treatment. It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying. Our approach uses an end-to- end trainable fully convolutional network that simultaneously estimates stem positions as well as the covered area of crops and weeds. It jointly learns the class-wise stem detection and the pixel-wise semantic segmentation. Experimental evaluations on different real-world datasets show that our approach is able to reliably solve this problem. Compared to state-of-the-art approaches, our approach not only substantially improves the stem detection accuracy, i.e., distinguishing crop and weed stems, but also provides an improvement in the semantic segmentation performance.
    @InProceedings{lottes2018iros,
    author = {P. Lottes and J. Behley and N. Chebrolu and A. Milioto and C. Stachniss},
    title = {Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming},
    booktitle = {Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = 2018,
    url = {http://www.ipb.uni-bonn.de/pdfs/lottes18iros.pdf},
    videourl = {https://www.youtube.com/watch?v=C9mjZxE_Sxg},
    abstract = {Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need the ability to identify crops and weeds in the field and must additionally select effective treatments. While certain types of weed can be treated mechanically, other types need to be treated by (selective) spraying. In this paper, we present an approach that provides the necessary information for effective plant-specific treatment. It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying. Our approach uses an end-to- end trainable fully convolutional network that simultaneously estimates stem positions as well as the covered area of crops and weeds. It jointly learns the class-wise stem detection and the pixel-wise semantic segmentation. Experimental evaluations on different real-world datasets show that our approach is able to reliably solve this problem. Compared to state-of-the-art approaches, our approach not only substantially improves the stem detection accuracy, i.e., distinguishing crop and weed stems, but also provides an improvement in the semantic segmentation performance.}
    }
  • J. Behley and C. Stachniss, “Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments,” in Proc. of Robotics: Science and Systems (RSS), 2018.
    [BibTeX] [PDF] [Video]
    @InProceedings{behley2018rss,
    author = {J. Behley and C. Stachniss},
    title = {Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments},
    booktitle = rss,
    year = 2018,
    videourl = {https://www.youtube.com/watch?v=-AEX203rXkE},
    url = {http://www.roboticsproceedings.org/rss14/p16.pdf},
    }

2015

  • J. Behley, V. Steinhage, and A. B. Cremers, “Efficient Radius Neighbor Search in Three-dimensional Point Clouds,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2015.
    [BibTeX]
    @inproceedings{behley2015icra,
    author = {J. Behley and V. Steinhage and A.B. Cremers},
    title = {{Efficient Radius Neighbor Search in Three-dimensional Point Clouds}},
    booktitle = icra,
    year = 2015,
    }

2013

  • J. Behley, V. Steinhage, and A. B. Cremers, “Laser-based Segment Classification Using a Mixture of Bag-of-Words,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2013.
    [BibTeX]
    @inproceedings{behley2013iros,
    author = {J. Behley and V. Steinhage and A.B. Cremers},
    title = {{Laser-based Segment Classification Using a Mixture of Bag-of-Words}},
    booktitle = iros,
    year = 2013,
    }
  • V. Steinhage, J. Behley, S. Meisel, and A. B. Cremers, “Reconstruction by components for automated updating of 3D city models,” Applied Geomatics, vol. 5, p. 285–298, 2013.
    [BibTeX]
    @article{steinhage2013ag,
    author = {V. Steinhage and J. Behley and S. Meisel and A.B. Cremers},
    title = {{Reconstruction by components for automated updating of 3D city models}},
    journal = {Applied Geomatics},
    volume = {5},
    pages = {285--298},
    year = {2013},
    }

2012

  • J. Behley, V. Steinhage, and A. B. Cremers, “Performance of Histogram Descriptors for the Classification of 3D Laser Range Data in Urban Environments,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2012.
    [BibTeX]
    @inproceedings{behley2012icra,
    author = {J. Behley and V. Steinhage and A.B. Cremers},
    title = {{Performance of Histogram Descriptors for the Classification of 3D Laser Range Data in Urban Environments}},
    booktitle = icra,
    year = 2012,
    }

2011

  • F. Schöler, J. Behley, V. Steinhage, D. Schulz, and A. B. Cremers, “Person Tracking in Three-Dimensional Laser Range Data with Explicit Occlusion Adaption,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2011.
    [BibTeX]
    @inproceedings{schoeler2011icra,
    author = {F. Sch{\"o}ler and J. Behley and V. Steinhage and D. Schulz and A.B. Cremers},
    title = {{Person Tracking in Three-Dimensional Laser Range Data with Explicit Occlusion Adaption}},
    booktitle = icra,
    year = 2011,
    }

2010

  • J. Behley, K. Kersting, D. Schulz, V. Steinhage, and A. B. Cremers, “Learning to Hash Logistic Regression for Fast 3D Scan Point Classification,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2010.
    [BibTeX]
    @inproceedings{behley2010iros,
    author = {J. Behley and K. Kersting and D. Schulz and V. Steinhage and A.B. Cremers},
    title = {{Learning to Hash Logistic Regression for Fast 3D Scan Point Classification}},
    booktitle = iros,
    year = 2010,
    }
  • V. Steinhage, J. Behley, S. Meisel, and A. B. Cremers, “Learning to Hash Logistic Regression for Fast 3D Scan Point Classification,” in Proc. of the ISPRS-Workshop on Core Spatial Databases – Updating, Maintenance and Services, 2010.
    [BibTeX]
    @inproceedings{steinhage2010isprs,
    author = {V. Steinhage and J. Behley and S. Meisel and A. B. Cremers},
    title = {{Learning to Hash Logistic Regression for Fast 3D Scan Point Classification}},
    booktitle = {Proc. of the ISPRS-Workshop on Core Spatial Databases - Updating, Maintenance and Services},
    year = 2010,
    }

2009

  • J. Behley and V. Steinhage, “Generation of 3D City Models Using Domain-Specific Information Fusion,” in Proc. of the International Conference on Computer Vision Systems (ICVS), 2009.
    [BibTeX]
    @inproceedings{behley2009icvs,
    author = {J. Behley and V. Steinhage},
    title = {{Generation of 3D City Models Using Domain-Specific Information Fusion}},
    booktitle = {Proc. of the International Conference on Computer Vision Systems (ICVS)},
    year = 2009,
    }