Biography

I am currently a M.S. student at Institute of Cyber Systems and Control, Department of Control Science and Engineering, Zhejiang University. I received my B.S. degree from College of Information Engineering from Zhejiang University of Technology in 2020. My latest research interests include SLAM and sensor fusion.

Research and Interests

• SLAM
• Sensor fusion

Publications

• Chao Chen, Hangyu Wu, Yukai Ma, Jiajun Lv, Laijian Li, and Yong Liu. LiDAR-Inertial SLAM with Efficiently Extracted Planes. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1497-1504, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  This paper proposes a LiDAR-Inertial SLAM with efficiently extracted planes, which couples explicit planes in the odometry to improve accuracy and in the mapping for consistency. The proposed method consists of three parts: an efficient Point →Line→Plane extraction algorithm, a LiDAR-Inertial-Plane tightly coupled odometry, and a global plane-aided mapping. Specifically, we leverage the ring field of the LiDAR point cloud to accelerate the region-growing-based plane extraction algorithm. Then we tightly coupled IMU pre-integration factors, LiDAR odometry factors, and explicit plane factors in the sliding window to obtain a more accurate initial pose for mapping. Finally, we maintain explicit planes in the global map, and enhance system consistency by optimizing the factor graph of optimized odometry factors and plane observation factors. Experimental results show that our plane extraction method is efficient, and the proposed plane-aided LiDAR-Inertial SLAM significantly improves the accuracy and consistency compared to the other state-of-the-art algorithms with only a small increase in time consumption.

  @inproceedings{chen2023lidar,
  title = {LiDAR-Inertial SLAM with Efficiently Extracted Planes},
  author = {Chao Chen and Hangyu Wu and Yukai Ma and Jiajun Lv and Laijian Li and Yong Liu},
  year = 2023,
  booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {1497-1504},
  doi = {10.1109/IROS55552.2023.10342325},
  abstract = {This paper proposes a LiDAR-Inertial SLAM with efficiently extracted planes, which couples explicit planes in the odometry to improve accuracy and in the mapping for consistency. The proposed method consists of three parts: an efficient Point →Line→Plane extraction algorithm, a LiDAR-Inertial-Plane tightly coupled odometry, and a global plane-aided mapping. Specifically, we leverage the ring field of the LiDAR point cloud to accelerate the region-growing-based plane extraction algorithm. Then we tightly coupled IMU pre-integration factors, LiDAR odometry factors, and explicit plane factors in the sliding window to obtain a more accurate initial pose for mapping. Finally, we maintain explicit planes in the global map, and enhance system consistency by optimizing the factor graph of optimized odometry factors and plane observation factors. Experimental results show that our plane extraction method is efficient, and the proposed plane-aided LiDAR-Inertial SLAM significantly improves the accuracy and consistency compared to the other state-of-the-art algorithms with only a small increase in time consumption.}
  }