Xieyuanli Chen

PhD Student
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: Google Scholar | Research Gate | Github

Short CV

Xieyuanli Chen is a PhD student at the University of Bonn. He received his Master degree in Robotics in 2017 at the National University of Defense Technology, China. During that time, 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 at Hunan University, China.

He is now also a member of the Technical Committee of RoboCup Rescue Robot League (RRL).

Research Interests

  • Robotics
  • Localization, Mapping, SLAM

Code releases

Teaching

  • Master Project – Visual LiDAR Odometry, Both Semesters, 2020
  • Sensors and State Estimation, Both Semesters, 2019/2020
  • Master Project – Semantic Place Categorization, Both Semesters, 2019

Awards

  • 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
  • Best in Class Small Robot Mobility in Rescue Robot League at RoboCup 2016

Publications

2020

  • 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,” Arxiv preprint, 2020.
    [BibTeX] [PDF]

    Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources. Approaches that operate directly on the point cloud use complex spatial aggregation operations, which are very expensive and difficult to optimize for embedded platforms. They are therefore not suitable for real-time applications with embedded systems. As an alternative, projection-based methods are more efficient and can run on embedded platforms. However, the current state-of-the-art projection-based methods do not achieve the same accuracy as point-based methods and use millions of parameters. In this paper, we therefore propose a projection-based method, called Multi-scale Interaction Network (MINet), which is very efficient and accurate. The network uses multiple paths with different scales and balances the computational resources between the scales. Additional dense interactions between the scales avoid redundant computations and make the network highly efficient. The proposed network outperforms point-based, image-based, and projection-based methods in terms of accuracy, number of parameters, and runtime. Moreover, the network processes more than 24 scans per second on an embedded platform, which is higher than the framerates of LiDAR sensors. The network is therefore suitable for autonomous vehicles.

    @article{li2020arxiv,
    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 = arxiv,
    year = 2020,
    eprint = {2008.09162v1},
    url = {http://arxiv.org/pdf/2008.09162v1},
    keywords = {cs.CV},
    abstract = {Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources. Approaches that operate directly on the point cloud use complex spatial aggregation operations, which are very expensive and difficult to optimize for embedded platforms. They are therefore not suitable for real-time applications with embedded systems. As an alternative, projection-based methods are more efficient and can run on embedded platforms. However, the current state-of-the-art projection-based methods do not achieve the same accuracy as point-based methods and use millions of parameters. In this paper, we therefore propose a projection-based method, called Multi-scale Interaction Network (MINet), which is very efficient and accurate. The network uses multiple paths with different scales and balances the computational resources between the scales. Additional dense interactions between the scales avoid redundant computations and make the network highly efficient. The proposed network outperforms point-based, image-based, and projection-based methods in terms of accuracy, number of parameters, and runtime. Moreover, the network processes more than 24 scans per second on an embedded platform, which is higher than the framerates of LiDAR sensors. The network is therefore suitable for autonomous vehicles.}
    }

  • 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.
    [BibTeX] [PDF]
    @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},
    url={http://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
    }

  • 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.
    [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 = {Proceedings 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 Proceedings 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 = {Proceedings 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},
    }