Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interest is Simultaneous Localization And Mapping(SLAM).

Research and Interests

• Simultaneous Localization
• Mapping(SLAM)

Publications

• 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.}
  }

• Jianxin Huang, Laijian Li, Xiangrui Zhao, Xiaolei Lang, Deye Zhu, and Yong Liu. LODM: Large-scale Online Dense Mapping for UAV. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022.
  [BibTeX] [Abstract] [DOI] [PDF]

  This paper proposes a method for online large-scale dense mapping. The UAV is within a range of 150-250 meters, combining GPS and visual odometry to estimate the scaled pose and sparse points. In order to use the depth of sparse points for depth map, we propose Sparse Confidence Cascade View-Aggregation MVSNet (SCCVA-MVSNet), which projects the depth-converged points in the sliding window on keyframes to obtain a sparse depth map. The photometric error constructs sparse confidence. The coarse depth and confidence through normalized convolution use the images of all keyframes, coarse depth, and confidence as the input of CVA-MVSNet to extract features and construct 3D cost volumes with adaptive view aggregation to balance the different stereo baselines between the keyframes. Our proposed network utilizes sparse features point information, the output of the network better maintains the consistency of the scale. Our experiments show that MVSNet using sparse feature point information outperforms image-only MVSNet, and our online reconstruction results are comparable to offline reconstruction methods. To benefit the research community, we open-source our code at https://github.com/hjxwhy/LODM.git

  @inproceedings{huang2022lls,
  title = {LODM: Large-scale Online Dense Mapping for UAV},
  author = {Jianxin Huang and Laijian Li and Xiangrui Zhao and Xiaolei Lang and Deye Zhu and Yong Liu},
  year = 2022,
  booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  doi = {10.1109/IROS47612.2022.9981994},
  abstract = {This paper proposes a method for online large-scale dense mapping. The UAV is within a range of 150-250 meters, combining GPS and visual odometry to estimate the scaled pose and sparse points. In order to use the depth of sparse points for depth map, we propose Sparse Confidence Cascade View-Aggregation MVSNet (SCCVA-MVSNet), which projects the depth-converged points in the sliding window on keyframes to obtain a sparse depth map. The photometric error constructs sparse confidence. The coarse depth and confidence through normalized convolution use the images of all keyframes, coarse depth, and confidence as the input of CVA-MVSNet to extract features and construct 3D cost volumes with adaptive view aggregation to balance the different stereo baselines between the keyframes. Our proposed network utilizes sparse features point information, the output of the network better maintains the consistency of the scale. Our experiments show that MVSNet using sparse feature point information outperforms image-only MVSNet, and our online reconstruction results are comparable to offline reconstruction methods. To benefit the research community, we open-source our code at https://github.com/hjxwhy/LODM.git}
  }