Address

Institute of Cyber-Systems and Control, Yuquan Campus, Zhejiang University, Hangzhou, Zhejiang, China

Contact Information

Email: 1924522729@qq.com

Chao Chen

MS Student

Institute of Cyber-Systems and Control, Zhejiang University, China

Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include Vision, laser and motion control.

Research and Interests

  • Vision
  • Laser
  • Motion control

Publications

  • Xiaolei Lang, Chao Chen, Kai Tang, Yukai Ma, Jiajun Lv, Yong Liu, and Xingxing Zuo. Coco-LIC: Continuous-Time Tightly-Coupled LiDAR-Inertial-Camera Odometry using Non-Uniform B-spline. IEEE Robotics and Automation Letters, 8:7074-7081, 2023.
    [BibTeX] [Abstract] [DOI] [PDF]
    In this paper, we propose an effcient continuous-time LiDAR-Inertial-Camera Odometry, utilizing non-uniform B-splines to tightly couple measurements from the LiDAR, IMU, and camera. In contrast to uniform B-spline-based continuous-time methods, our non-uniform B-spline approach offers signifcant advantages in terms of achieving real-time effciency and high accuracy. This is accomplished by dynamically and adaptively placing control points, taking into account the varying dynamics of the motion. To enable effcient fusion of heterogeneous LiDAR-Inertial-Camera data within a short sliding-window optimization, we assign depth to visual pixels using corresponding map points from a global LiDAR map, and formulate frame-to-map reprojection factors for the associated pixels in the current image frame. This way circumvents the necessity for depth optimization of visual pixels, which typically entails a lengthy sliding window with numerous control points for continuous-time trajectory estimation. We conduct dedicated experiments on real-world datasets to demonstrate the advantage and effcacy of adopting non-uniform continuous-time trajectory representation. Our LiDAR-Inertial-Camera odometry system is also extensively evaluated on both challenging scenarios with sensor degenerations and large-scale scenarios, and has shown comparable or higher accuracy than the state-of-the-art methods. The codebase of this paper will also be open-sourced at https://github.com/APRIL-ZJU/Coco-LIC .
    @article{lang2023lic,
    title = {Coco-LIC: Continuous-Time Tightly-Coupled LiDAR-Inertial-Camera Odometry using Non-Uniform B-spline},
    author = {Xiaolei Lang and Chao Chen and Kai Tang and Yukai Ma and Jiajun Lv and Yong Liu and Xingxing Zuo},
    year = 2023,
    journal = {IEEE Robotics and Automation Letters},
    volume = 8,
    pages = {7074-7081},
    doi = {10.1109/LRA.2023.3315542},
    abstract = {In this paper, we propose an effcient continuous-time LiDAR-Inertial-Camera Odometry, utilizing non-uniform B-splines to tightly couple measurements from the LiDAR, IMU, and camera. In contrast to uniform B-spline-based continuous-time methods, our non-uniform B-spline approach offers signifcant advantages in terms of achieving real-time effciency and high accuracy. This is accomplished by dynamically and adaptively placing control points, taking into account the varying dynamics of the motion. To enable effcient fusion of heterogeneous LiDAR-Inertial-Camera data within a short sliding-window optimization, we assign depth to visual pixels using corresponding map points from a global LiDAR map, and formulate frame-to-map reprojection factors for the associated pixels in the current image frame. This way circumvents the necessity for depth optimization of visual pixels, which typically entails a lengthy sliding window with numerous control points for continuous-time trajectory estimation. We conduct dedicated experiments on real-world datasets to demonstrate the advantage and effcacy of adopting non-uniform continuous-time trajectory representation. Our LiDAR-Inertial-Camera odometry system is also extensively evaluated on both challenging scenarios with sensor degenerations and large-scale scenarios, and has shown comparable or higher accuracy than the state-of-the-art methods. The codebase of this paper will also be open-sourced at https://github.com/APRIL-ZJU/Coco-LIC .}
    }
  • Laijian Li, Yukai Ma, Kai Tang, Xiangrui Zhao, Chao Chen, Jianxin Huang, Jianbiao Mei, and Yong Liu. Geo-localization with Transformer-based 2D-3D match Network. IEEE Robotics and Automation Letters (RA-L), 8:4855-4862, 2023.
    [BibTeX] [Abstract] [DOI] [PDF]
    This letter presents a novel method for geographical localization by registering satellite maps with LiDAR point clouds. This method includes a Transformer-based 2D-3D matching network called D-GLSNet that directly matches the LiDAR point clouds and satellite images through end-to-end learning. Without the need for feature point detection, D-GLSNet provides accurate pixel-to-point association between the LiDAR point clouds and satellite images. And then, we can easily calculate the horizontal offset (Δx,Δy) and angular deviation Δθyaw between them, thereby achieving accurate registration. To demonstrate our network’s localization potential, we have designed a Geo-localization Node (GLN) that implements geographical localization and is plug-and-play in the SLAM system. Compared to GPS, GLN is less susceptible to external interference, such as building occlusion. In urban scenarios, our proposed D-GLSNet can output high-quality matching, enabling GLN to function stably and deliver more accurate localization results. Extensive experiments on the KITTI dataset show that our D-GLSNet method achieves a mean Relative Translation Error (RTE) of 1.43 m. Furthermore, our method outperforms state-of-the-art LiDAR-based geospatial localization methods when combined with odometry.
    @article{li2023glw,
    title = {Geo-localization with Transformer-based 2D-3D match Network},
    author = {Laijian Li and Yukai Ma and Kai Tang and Xiangrui Zhao and Chao Chen and Jianxin Huang and Jianbiao Mei and Yong Liu},
    year = 2023,
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    volume = 8,
    pages = {4855-4862},
    doi = {10.1109/LRA.2023.3290526},
    abstract = {This letter presents a novel method for geographical localization by registering satellite maps with LiDAR point clouds. This method includes a Transformer-based 2D-3D matching network called D-GLSNet that directly matches the LiDAR point clouds and satellite images through end-to-end learning. Without the need for feature point detection, D-GLSNet provides accurate pixel-to-point association between the LiDAR point clouds and satellite images. And then, we can easily calculate the horizontal offset (Δx,Δy) and angular deviation Δθyaw between them, thereby achieving accurate registration. To demonstrate our network's localization potential, we have designed a Geo-localization Node (GLN) that implements geographical localization and is plug-and-play in the SLAM system. Compared to GPS, GLN is less susceptible to external interference, such as building occlusion. In urban scenarios, our proposed D-GLSNet can output high-quality matching, enabling GLN to function stably and deliver more accurate localization results. Extensive experiments on the KITTI dataset show that our D-GLSNet method achieves a mean Relative Translation Error (RTE) of 1.43 m. Furthermore, our method outperforms state-of-the-art LiDAR-based geospatial localization methods when combined with odometry.}
    }
  • Chao Chen, Yukai Ma, Jiajun Lv, Xiangrui Zhao, Laijian Li, Yong Liu, and Wang Gao. OL-SLAM: A Robust and Versatile System of Object Localization and SLAM. Sensors, 23:801, 2023.
    [BibTeX] [Abstract] [DOI] [PDF]
    This paper proposes a real-time, versatile Simultaneous Localization and Mapping (SLAM) and object localization system, which fuses measurements from LiDAR, camera, Inertial Measurement Unit (IMU), and Global Positioning System (GPS). Our system can locate itself in an unknown environment and build a scene map based on which we can also track and obtain the global location of objects of interest. Precisely, our SLAM subsystem consists of the following four parts: LiDAR-inertial odometry, Visual-inertial odometry, GPS-inertial odometry, and global pose graph optimization. The target-tracking and positioning subsystem is developed based on YOLOv4. Benefiting from the use of GPS sensor in the SLAM system, we can obtain the global positioning information of the target; therefore, it can be highly useful in military operations, rescue and disaster relief, and other scenarios.
    @article{chen2023ols,
    title = {OL-SLAM: A Robust and Versatile System of Object Localization and SLAM},
    author = {Chao Chen and Yukai Ma and Jiajun Lv and Xiangrui Zhao and Laijian Li and Yong Liu and Wang Gao},
    year = 2023,
    journal = {Sensors},
    volume = 23,
    pages = {801},
    doi = {10.3390/s23020801},
    abstract = {This paper proposes a real-time, versatile Simultaneous Localization and Mapping (SLAM) and object localization system, which fuses measurements from LiDAR, camera, Inertial Measurement Unit (IMU), and Global Positioning System (GPS). Our system can locate itself in an unknown environment and build a scene map based on which we can also track and obtain the global location of objects of interest. Precisely, our SLAM subsystem consists of the following four parts: LiDAR-inertial odometry, Visual-inertial odometry, GPS-inertial odometry, and global pose graph optimization. The target-tracking and positioning subsystem is developed based on YOLOv4. Benefiting from the use of GPS sensor in the SLAM system, we can obtain the global positioning information of the target; therefore, it can be highly useful in military operations, rescue and disaster relief, and other scenarios.}
    }