Xieyuanli Chen

Dr.-Ing.
Contact:
Email: xieyuanli.chen@nulligg.uni-bonn.de
Tel: +49 – 228 – 73 – 29 10
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.011
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Profiles: New HomepageGoogle Scholar | Research Gate | Github

Short CV

Xieyuanli Chen received his Doctoral degree in 2022 at StachnissLab, the University of Bonn. He received his Master degree in Robotics in 2017. During his Master’s studies, he was a member of the Organizing Committee of RoboCup Rescue Robot League. He received his Bachelor degree in Electrical Engineering and Automation in 2015. He is also a member of the Technical Committee of RoboCup Rescue Robot League (RRL).

[Extended CV].

Research Interests

  • Robotics, Localization, Mapping, SLAM, Mobile Robots, Robot Learning

Awards

  • Doctoral Degree with Distinction – summa cum laude – Highest Possible Score. [PDF][Website]
  • Robotics: Science and Systems (RSS) Pioneer 2021. [PDF] [Poster] [Website]
  • Best-in-Class Exploration and Mapping in Rescue Robot League at RoboCup 2021
  • Nomination of Best System Paper at Robotics: Science and Systems (RSS) 2020
  • Ph. D. student Scholarship from China Scholarship Council (CSC) 2018
  • Winner of Rescue Robot Competition at IEEE SSRR 2017
  • In recognition of Exceptional Performance as Associate Judge in RoboCup RRL 2017
  • Best-in-Class Small Robot Mobility in Rescue Robot League at RoboCup 2016

Teaching

  • Supervising Intern Projects: LiDAR Place Recognition [PDF], LiDAR Moving Object Segmentation [PDF], 2022
  • Supervising Intern Projects: Pole-based Localization [PDF], Static Map Generation [PDF], 2021/2022
  • Mobile Sensing and Robotics, Both Semesters, 2020/2021
  • Supervising Master Project: Visual LiDAR Odometry, Both Semesters, 2020
  • Sensors and State Estimation, Both Semesters, 2019/2020
  • Supervising Master Project: Semantic Place Categorization, Both Semesters, 2019
  • Supervising Master Thesis: Hao Dong, Excellent, 2022
    Deep Learning-based Pole Extractor for Long-term LiDAR Global Localization
  • Supervising Bachelor Thesis: Verena Fitzke, Excellent, 2020
    Extracting color and semantic information for 3D LiDAR point clouds from camera images

Code releases

Publications

2022

  • X. Chen, “LiDAR-Based Semantic Perception for Autonomous Vehicles,” PhD Thesis, 2022.
    [BibTeX] [PDF]
    @phdthesis{chen2022phd,
    author = {Xieyuanli Chen},
    title = {{LiDAR-Based Semantic Perception for Autonomous Vehicles}},
    school = {University of Bonn},
    year = 2022,
    month = sep,
    url = {https://hdl.handle.net/20.500.11811/10228},
    urn = https://nbn-resolving.org/urn:nbn:de:hbz:5-67873,
    }

  • J. Sun, Y. Wang, M. Feng, D. Wang, J. Zhao, C. Stachniss, and X. Chen, “ICK-Track: A Category-Level 6-DoF Pose Tracker Using Inter-Frame Consistent Keypoints for Aerial Manipulation,” in Proc. of the ieee/rsj int. conf. on intelligent robots and systems (iros), 2022.
    [BibTeX] [PDF] [Code]
    @inproceedings{sun2022iros,
    title = {{ICK-Track: A Category-Level 6-DoF Pose Tracker Using Inter-Frame Consistent Keypoints for Aerial Manipulation}},
    author = {Jingtao Sun and Yaonan Wang and Mingtao Feng and Danwei Wang and Jiawen Zhao and Cyrill Stachniss and Xieyuanli Chen},
    booktitle = {Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = {2022},
    codeurl = {https://github.com/S-JingTao/ICK-Track}
    }

  • L. Nunes, X. Chen, R. Marcuzzi, A. Osep, L. Leal-Taixé, C. Stachniss, and J. Behley, “Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles,” Ieee robotics and automation letters (ra-l), 2022. doi:10.1109/LRA.2022.3187872
    [BibTeX] [PDF] [Code]
    @article{nunes2022ral-3duis,
    author = {Lucas Nunes and Xieyuanli Chen and Rodrigo Marcuzzi and Aljosa Osep and Laura Leal-Taixé and Cyrill Stachniss and Jens Behley},
    title = {{Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles}},
    journal = ral,
    url = {https://www.ipb.uni-bonn.de/pdfs/nunes2022ral-iros.pdf},
    codeurl = {https://github.com/PRBonn/3DUIS},
    doi = {10.1109/LRA.2022.3187872},
    year = 2022
    }

  • B. Mersch, X. Chen, I. Vizzo, L. Nunes, J. Behley, and C. Stachniss, “Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions,” Ieee robotics and automation letters (ra-l), vol. 7, iss. 3, p. 7503–7510, 2022. doi:10.1109/LRA.2022.3183245
    [BibTeX] [PDF] [Code] [Video]
    @article{mersch2022ral,
    author = {B. Mersch and X. Chen and I. Vizzo and L. Nunes and J. Behley and C. Stachniss},
    title = {{Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions}},
    journal = ral,
    year = 2022,
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/mersch2022ral.pdf},
    volume = {7},
    number = {3},
    pages = {7503--7510},
    doi = {10.1109/LRA.2022.3183245},
    codeurl = {https://github.com/PRBonn/4DMOS},
    videourl = {https://youtu.be/5aWew6caPNQ},
    }

  • T. Guadagnino, X. Chen, M. Sodano, J. Behley, G. Grisetti, and C. Stachniss, “Fast Sparse LiDAR Odometry Using Self-Supervised Feature Selection on Intensity Images,” Ieee robotics and automation letters (ra-l), vol. 7, iss. 3, pp. 7597-7604, 2022. doi:10.1109/LRA.2022.3184454
    [BibTeX] [PDF]
    @article{guadagnino2022ral,
    author = {T. Guadagnino and X. Chen and M. Sodano and J. Behley and G. Grisetti and C. Stachniss},
    title = {{Fast Sparse LiDAR Odometry Using Self-Supervised Feature Selection on Intensity Images}},
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    year = 2022,
    volume = {7},
    number = {3},
    pages = {7597-7604},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/guadagnino2022ral-iros.pdf},
    issn = {2377-3766},
    doi = {10.1109/LRA.2022.3184454}
    }

  • X. Chen, B. Mersch, L. Nunes, R. Marcuzzi, I. Vizzo, J. Behley, and C. Stachniss, “Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation,” Ieee robotics and automation letters (ra-l), vol. 7, iss. 3, pp. 6107-6114, 2022. doi:10.1109/LRA.2022.3166544
    [BibTeX] [PDF] [Code]
    @article{chen2022ral,
    author = {X. Chen and B. Mersch and L. Nunes and R. Marcuzzi and I. Vizzo and J. Behley and C. Stachniss},
    title = {{Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation}},
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    year = 2022,
    volume = 7,
    number = 3,
    pages = {6107-6114},
    url = {http://arxiv.org/pdf/2201.04501},
    issn = {2377-3766},
    doi = {10.1109/LRA.2022.3166544},
    codeurl = {https://github.com/PRBonn/auto-mos},
    }

  • S. Yang, L. Zheng, X. Chen, L. Zabawa, M. Zhang, and M. Wang, “Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod,” in Proc.~of the ieee/cvf conf.~on computer vision and pattern recognition workshops, 2022, pp. 1666-1675.
    [BibTeX] [PDF]
    @inproceedings{yang2022cvprws,
    author = {Yang, Si and Zheng, Lihua and Chen, Xieyuanli and Zabawa, Laura and Zhang, Man and Wang, Minjuan},
    title = {{Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod}},
    booktitle = {Proc.~of the IEEE/CVF Conf.~on Computer Vision and Pattern Recognition Workshops},
    url={https://openaccess.thecvf.com/content/CVPR2022W/AgriVision/papers/Yang_Transfer_Learning_From_Synthetic_In-Vitro_Soybean_Pods_Dataset_for_In-Situ_CVPRW_2022_paper.pdf},
    pages={1666-1675},
    year = 2022,
    }

  • L. Nunes, R. Marcuzzi, X. Chen, J. Behley, and C. Stachniss, “SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination,” Ieee robotics and automation letters (ra-l), vol. 7, iss. 2, pp. 2116-2123, 2022. doi:10.1109/LRA.2022.3142440
    [BibTeX] [PDF] [Code]
    @article{nunes2022ral,
    author = {L. Nunes and R. Marcuzzi and X. Chen and J. Behley and C. Stachniss},
    title = {{SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination}},
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    year = 2022,
    doi = {10.1109/LRA.2022.3142440},
    issn = {2377-3766},
    volume = {7},
    number = {2},
    pages = {2116-2123},
    url = {http://www.ipb.uni-bonn.de/pdfs/nunes2022ral-icra.pdf},
    codeurl = {https://github.com/PRBonn/segcontrast}
    }

  • S. Li, X. Chen, Y. Liu, D. Dai, C. Stachniss, and J. Gall, “Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform,” Ieee robotics and automation letters (ra-l), vol. 7, iss. 2, pp. 738-745, 2022. doi:10.1109/LRA.2021.3132059
    [BibTeX] [PDF] [Code]
    @article{li2022ral,
    author = {S. Li and X. Chen and Y. Liu and D. Dai and C. Stachniss and J. Gall},
    title = {{Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform}},
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    year = 2022,
    doi = {10.1109/LRA.2021.3132059},
    issn = {2377-3766},
    volume = 7,
    number = 2,
    pages = {738-745},
    codeurl = {https://github.com/sj-li/MINet},
    }

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},
    }

  • M. Arora, L. Wiesmann, X. Chen, and C. Stachniss, “Mapping the Static Parts of Dynamic Scenes from 3D LiDAR Point Clouds Exploiting Ground Segmentation,” in Proc. of the european conf. on mobile robots (ecmr), 2021.
    [BibTeX] [PDF] [Code]
    @InProceedings{arora2021ecmr,
    author = {M. Arora and L. Wiesmann and X. Chen and C. Stachniss},
    title = {{Mapping the Static Parts of Dynamic Scenes from 3D LiDAR Point Clouds Exploiting Ground Segmentation}},
    booktitle = ecmr,
    codeurl = {https://github.com/humbletechy/Dynamic-Point-Removal},
    year = {2021},
    }

  • H. Dong, X. Chen, and C. Stachniss, “Online Range Image-based Pole Extractor for Long-term LiDAR Localization in Urban Environments,” in Proc. of the european conf. on mobile robots (ecmr), 2021.
    [BibTeX] [PDF] [Code]
    @InProceedings{dong2021ecmr,
    author = {H. Dong and X. Chen and C. Stachniss},
    title = {{Online Range Image-based Pole Extractor for Long-term LiDAR Localization in Urban Environments}},
    booktitle = ecmr,
    year = {2021},
    codeurl = {https://github.com/PRBonn/pole-localization},
    url = {https://www.ipb.uni-bonn.de/pdfs/dong2021ecmr.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, vol. 46, p. 61–81, 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 = {Autonomous Robots},
    year = {2021},
    doi = {10.1007/s10514-021-09999-0},
    issn = {1573-7527},
    volume=46,
    pages={61--81},
    codeurl = {https://github.com/PRBonn/OverlapNet},
    url = {http://www.ipb.uni-bonn.de/pdfs/chen2021auro.pdf}
    }

  • M. Zhou, X. Chen, N. Samano, C. Stachniss, and A. Calway, “Efficient localisation using images and openstreetmaps,” in Proc. of the ieee/rsj int. conf. on intelligent robots and systems (iros), 2021.
    [BibTeX] [PDF]
    @inproceedings{zhou2021iros,
    title = {Efficient Localisation Using Images and OpenStreetMaps},
    author = {Zhou, Mengjie and Chen, Xieyuanli and Samano, Noe and Stachniss, Cyrill and Calway, Andrew},
    booktitle = iros,
    year = {2021},
    url = {http://www.ipb.uni-bonn.de/pdfs/zhou2021iros.pdf}
    }

  • C. Shi, X. Chen, K. Huang, J. Xiao, H. Lu, and C. Stachniss, “Keypoint Matching for Point Cloud Registration using Multiplex Dynamic Graph Attention Networks,” Ieee robotics and automation letters (ra-l), vol. 6, pp. 8221-8228, 2021. doi:10.1109/LRA.2021.3097275
    [BibTeX] [PDF]
    @article{shi2021ral,
    title={{Keypoint Matching for Point Cloud Registration using Multiplex Dynamic Graph Attention Networks}},
    author={C. Shi and X. Chen and K. Huang and J. Xiao and H. Lu and C. Stachniss},
    year={2021},
    journal=ral,
    volume=6,
    issue=4,
    pages={8221-8228},
    doi = {10.1109/LRA.2021.3097275},
    issn = {2377-3766},
    }

  • 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},
    }

  • 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},
    }

  • A. Reinke, X. Chen, and C. Stachniss, “Simple But Effective Redundant Odometry for Autonomous Vehicles,” in Proc. of the ieee int. conf. on robotics & automation (icra), 2021.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{reinke2021icra,
    title={{Simple But Effective Redundant Odometry for Autonomous Vehicles}},
    author={A. Reinke and X. Chen and C. Stachniss},
    booktitle=icra,
    year=2021,
    url = {http://www.ipb.uni-bonn.de/pdfs/reinke2021icra.pdf},
    codeurl = {https://github.com/PRBonn/MutiverseOdometry},
    videourl = {https://youtu.be/zLpnPEyDKfM}
    }

  • 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

  • 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},
    }

  • 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},
    }

2019

  • 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},
    }