Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include deep learning in SLAM, depth prediction.

Research and Interests

• Deep learning in SLAM
• Depth prediction

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.}
  }

• Xiaolei Lang, Jiajun Lv, Jianxin Huang, Yukai Ma, Yong Liu, and Xingxing Zuo. Ctrl-VIO: Continuous-Time Visual-Inertial Odometry for Rolling Shutter Cameras. IEEE Robotics and Automation Letters (RA-L), 7(4):11537-11544, 2022.
  [BibTeX] [Abstract] [DOI] [PDF]

  In this letter, we propose a probabilistic continuoustime visual-inertial odometry (VIO) for rolling shutter cameras. The continuous-time trajectory formulation naturally facilitates the fusion of asynchronized high-frequency IMU data and motion distorted rolling shutter images. To prevent intractable computation load, the proposed VIO is sliding-window and keyframe-based. We propose to probabilistically marginalize the control points to keep the constant number of keyframes in the sliding window. Furthermore, the line exposure time difference (line delay) of the rolling shutter camera can be online calibrated in our continuous-time VIO. To extensively examine the performance of our continuoustime VIO, experiments are conducted on publicly-available WHURSVI, TUM-RSVI, and SenseTime-RSVI rolling shutter datasets. The results demonstrate the proposed continuous-time VIO significantly outperforms the existing state-of-the-art VIO methods.

  @article{lang2022ctv,
  title = {Ctrl-VIO: Continuous-Time Visual-Inertial Odometry for Rolling Shutter Cameras},
  author = {Xiaolei Lang and Jiajun Lv and Jianxin Huang and Yukai Ma and Yong Liu and Xingxing Zuo},
  year = 2022,
  journal = {IEEE Robotics and Automation Letters (RA-L)},
  volume = {7},
  number = {4},
  pages = {11537-11544},
  doi = {10.1109/LRA.2022.3202349},
  abstract = {In this letter, we propose a probabilistic continuoustime visual-inertial odometry (VIO) for rolling shutter cameras. The continuous-time trajectory formulation naturally facilitates
  the fusion of asynchronized high-frequency IMU data and motion distorted rolling shutter images. To prevent intractable computation load, the proposed VIO is sliding-window and keyframe-based. We propose to probabilistically marginalize the control points to keep the constant number of keyframes in the sliding window. Furthermore, the line exposure time difference (line delay) of the rolling
  shutter camera can be online calibrated in our continuous-time VIO. To extensively examine the performance of our continuoustime VIO, experiments are conducted on publicly-available WHURSVI, TUM-RSVI, and SenseTime-RSVI rolling shutter datasets. The results demonstrate the proposed continuous-time VIO significantly outperforms the existing state-of-the-art VIO methods.}
  }

• 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}
  }

• Sen Lin, Jianxin Huang, Wenzhou Chen, Wenlong Zhou, Jinhong Xu, Yong Liu, and Jinqiang Yao. Intelligent warehouse monitoring based on distributed system and edge computing. International Journal of Intelligent Robotics and Applications, 5:130–142, 2021.
  [BibTeX] [Abstract] [DOI] [PDF]

  This paper mainly focuses on the volume calculation of materials in the warehouse where sand and gravel materials are stored and monitored whether materials are lacking in real-time. Specifically, we proposed the sandpile model and the point cloud projection obtained from the LiDAR sensors to calculate the material volume. We use distributed edge computing modules to build a centralized system and transmit data remotely through a high-power wireless network, which solves sensor placement and data transmission in a complex warehouse environment. Our centralized system can also reduce worker participation in a harsh factorial environment. Furthermore, the point cloud data of the warehouse is colored to visualize the actual factorial environment. Our centralized system has been deployed in the real factorial environment and got a good performance.

  @article{huang2021iwm,
  title = {Intelligent warehouse monitoring based on distributed system and edge computing},
  author = {Sen Lin and Jianxin Huang and Wenzhou Chen and Wenlong Zhou and Jinhong Xu and Yong Liu and Jinqiang Yao},
  year = 2021,
  journal = {International Journal of Intelligent Robotics and Applications},
  volume = 5,
  pages = {130--142},
  doi = {10.1007/s41315-021-00173-4},
  issue = 2,
  abstract = {This paper mainly focuses on the volume calculation of materials in the warehouse where sand and gravel materials are stored and monitored whether materials are lacking in real-time. Specifically, we proposed the sandpile model and the point cloud projection obtained from the LiDAR sensors to calculate the material volume. We use distributed edge computing modules to build a centralized system and transmit data remotely through a high-power wireless network, which solves sensor placement and data transmission in a complex warehouse environment. Our centralized system can also reduce worker participation in a harsh factorial environment. Furthermore, the point cloud data of the warehouse is colored to visualize the actual factorial environment. Our centralized system has been deployed in the real factorial environment and got a good performance.}
  }