Address

Room 101, Institute of Cyber-Systems and Control, Yuquan Campus, Zhejiang University, Hangzhou, Zhejiang, China

Contact Information

Email: lvjiajun314@zju.edu.cn

Jiajun Lv

PhD Student

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


Biography

I’m pursuing my Ph.D. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include extrinsic calibration between LiDAR, IMU and camera.

Research and Interests

Sensor Calibration
Sensor Fusion

Publications

  • Jiajun Lv, Kewei Hu, Jinhong Xu, Yong Liu, and Xingxing Zuo. CLINS: Continuous-Time Trajectory Estimation for LiDAR Inertial System. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021.
    [BibTeX]
    @inproceedings{lv2021cct,
    title = {CLINS: Continuous-Time Trajectory Estimation for LiDAR Inertial System},
    author = {Jiajun Lv and Kewei Hu and Jinhong Xu and Yong Liu and Xingxing Zuo},
    year = 2021,
    booktitle = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems}
    }
  • Jiajun Lv, Jinhong Xu, Kewei Hu, Yong Liu, and Xingxing Zuo. Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), page 9968–9975, 2020.
    [BibTeX] [Abstract] [DOI] [arXiv] [PDF]
    Sensor calibration is the fundamental block for a multi-sensor fusion system. This paper presents an accurate and repeatable LiDAR-IMU calibration method (termed LI-Calib), to calibrate the 6-DOF extrinsic transformation between the 3D LiDAR and the Inertial Measurement Unit (IMU). Regarding the high data capture rate for LiDAR and IMU sensors, LI-Calib adopts a continuous-time trajectory formulation based on B-Spline, which is more suitable for fusing high-rate or asynchronous measurements than discrete-time based approaches. Additionally, LI-Calib decomposes the space into cells and identifies the planar segments for data association, which renders the calibration problem well-constrained in usual scenarios without any artificial targets. We validate the proposed calibration approach on both simulated and real-world experiments. The results demonstrate the high accuracy and good repeatability of the proposed method in common human-made scenarios. To benefit the research community, we open-source our code at https://github.com/APRIL-ZJU/lidar_IMU_calib.
    @inproceedings{lv2020targetlessco,
    title = {Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation},
    author = {Jiajun Lv and Jinhong Xu and Kewei Hu and Yong Liu and Xingxing Zuo},
    year = 2020,
    booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    pages = {9968--9975},
    doi = {https://doi.org/10.1109/IROS45743.2020.9341405},
    abstract = {Sensor calibration is the fundamental block for a multi-sensor fusion system. This paper presents an accurate and repeatable LiDAR-IMU calibration method (termed LI-Calib), to calibrate the 6-DOF extrinsic transformation between the 3D LiDAR and the Inertial Measurement Unit (IMU). Regarding the high data capture rate for LiDAR and IMU sensors, LI-Calib adopts a continuous-time trajectory formulation based on B-Spline, which is more suitable for fusing high-rate or asynchronous measurements than discrete-time based approaches. Additionally, LI-Calib decomposes the space into cells and identifies the planar segments for data association, which renders the calibration problem well-constrained in usual scenarios without any artificial targets. We validate the proposed calibration approach on both simulated and real-world experiments. The results demonstrate the high accuracy and good repeatability of the proposed method in common human-made scenarios. To benefit the research community, we open-source our code at https://github.com/APRIL-ZJU/lidar_IMU_calib.},
    arxiv = {https://arxiv.org/pdf/2007.14759.pdf}
    }
  • Xingxing Zuo, Yulin Yang, Patrick Geneva, Jiajun Lv, Yong Liu, Guoquan Huang, and Marc Pollefeys. LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), page 5112–5119, 2020.
    [BibTeX] [Abstract] [arXiv] [PDF]
    Multi-sensor fusion of multi-modal measurements from commodity inertial, visual and LiDAR sensors to provide robust and accurate 6DOF pose estimation holds great potential in robotics and beyond. In this paper, building upon our prior work (i.e., LIC-Fusion), we develop a sliding-window filter based LiDAR-Inertial-Camera odometry with online spatiotemporal calibration (i.e., LIC-Fusion 2.0), which introduces a novel sliding-window plane-feature tracking for efficiently processing 3D LiDAR point clouds. In particular, after motion compensation for LiDAR points by leveraging IMU data, low-curvature planar points are extracted and tracked across the sliding window. A novel outlier rejection criteria is proposed in the plane-feature tracking for high quality data association. Only the tracked planar points belonging to the same plane will be used for plane initialization, which makes the plane extraction efficient and robust. Moreover, we perform the observability analysis for the IMU-LiDAR subsystem under consideration and report the degenerate cases for spatiotemporal calibration using plane features. While the estimation consistency and identified degenerate motions are validated in Monte-Carlo simulations, different real-world experiments are also conducted to show that the proposed LIC-Fusion 2.0 outperforms its predecessor and other state-of-the-art methods.
    @inproceedings{zuo2020licfusion2l,
    title = {LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking},
    author = {Xingxing Zuo and Yulin Yang and Patrick Geneva and Jiajun Lv and Yong Liu and Guoquan Huang and Marc Pollefeys},
    year = 2020,
    booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    pages = {5112--5119},
    abstract = {Multi-sensor fusion of multi-modal measurements from commodity inertial, visual and LiDAR sensors to provide robust and accurate 6DOF pose estimation holds great potential in robotics and beyond. In this paper, building upon our prior work (i.e., LIC-Fusion), we develop a sliding-window filter based LiDAR-Inertial-Camera odometry with online spatiotemporal calibration (i.e., LIC-Fusion 2.0), which introduces a novel sliding-window plane-feature tracking for efficiently processing 3D LiDAR point clouds. In particular, after motion compensation for LiDAR points by leveraging IMU data, low-curvature planar points are extracted and tracked across the sliding window. A novel outlier rejection criteria is proposed in the plane-feature tracking for high quality data association. Only the tracked planar points belonging to the same plane will be used for plane initialization, which makes the plane extraction efficient and robust. Moreover, we perform the observability analysis for the IMU-LiDAR subsystem under consideration and report the degenerate cases for spatiotemporal calibration using plane features. While the estimation consistency and identified degenerate motions are validated in Monte-Carlo simulations, different real-world experiments are also conducted to show that the proposed LIC-Fusion 2.0 outperforms its predecessor and other state-of-the-art methods.},
    arxiv = {https://arxiv.org/pdf/2008.07196.pdf}
    }
  • Jinhong Xu, Jiajun Lv, Zaishen Pan, Yong Liu, and Yinan Chen. Real-Time LiDAR Data Assocation Aided by IMU in High Dynamic Environment. In 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR), page 202–205, 2018.
    [BibTeX] [Abstract] [DOI]
    In recent years, with the breakthroughs in sensor technology, SLAM technology is developing towards high speed and high dynamic applications. The rotating multi line LiDAR sensor plays an important role. However, the rotating multi line LiDAR sensors need to restructure the data in high dynamic environment. Our work is to propose a LiDAR data correction method based on IMU and hardware synchronization, and make a hardware synchronization unit. This method can still output correct point cloud information when LiDAR sensor is moving violently.
    @inproceedings{xu2018realtimeld,
    title = {Real-Time LiDAR Data Assocation Aided by IMU in High Dynamic Environment},
    author = {Jinhong Xu and Jiajun Lv and Zaishen Pan and Yong Liu and Yinan Chen},
    year = 2018,
    booktitle = {2018 IEEE International Conference on Real-time Computing and Robotics (RCAR)},
    pages = {202--205},
    doi = {https://doi.org/10.1109/RCAR.2018.8621627},
    abstract = {In recent years, with the breakthroughs in sensor technology, SLAM technology is developing towards high speed and high dynamic applications. The rotating multi line LiDAR sensor plays an important role. However, the rotating multi line LiDAR sensors need to restructure the data in high dynamic environment. Our work is to propose a LiDAR data correction method based on IMU and hardware synchronization, and make a hardware synchronization unit. This method can still output correct point cloud information when LiDAR sensor is moving violently.}
    }