In this paper, we propose an accurate and robust multi-modal sensor fusion framework, MetroLoc, towards one of the most extreme scenarios, the large-scale metro environments. MetroLoc is built atop an IMU-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, and inertial information with the convenience of loosely coupled methods. The proposed framework is composed of three submodules: IMU odometry, LiDAR-inertial odometry (LIO), and Visual-inertial odometry (VIO). The IMU is treated as the primary sensor, which achieves the observations from LIO and VIO to constrain the accelerometer and gyroscope biases. Compared to previous point-only LIO methods, our approach leverages more geometry information by introducing both line and plane features into motion estimation. The VIO also utilizes the environmental structure information by employing both lines and points. Our proposed method has been tested in the long-during metro environments with a maintenance vehicle. Experimental results show the system more accurate and robust than the state-of-the-art approaches with real-time performance. The proposed method can reach 0.278% maximum drift in translation even in the highly degenerated tunnels. Besides, we develop a series of Virtual Reality (VR) applications towards efficient, economical, and interactive rail vehicle state and trackside infrastructure monitoring tasks.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    MetroLoc: Metro Vehicle Mapping and Localization With LiDAR-Camera-Inertial Integration


    Contributors:
    Wang, Yusheng (author) / Song, Weiwei (author) / Wang, Yapeng (author) / Dai, Xinye (author) / Lou, Yidong (author)


    Publication date :

    2025-02-01


    Size :

    5386748 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Vision-LiDAR-Inertial Localization and Mapping Dataset of a Mining Cave

    Zhou, Yang / Zhu, Shiqiang / Li, Yuehua | Springer Verlag | 2023


    Vision-LiDAR-Inertial Localization and Mapping Dataset of a Mining Cave

    Zhou, Yang / Zhu, Shiqiang / Li, Yuehua | TIBKAT | 2023


    An Enhanced LiDAR Inertial Localization and Mapping System for Unmanned Ground Vehicles

    Liu, Kangcheng / Zhou, Xunkuai / Chen, Ben M. | IEEE | 2022


    Camera-LIDAR Integration: Probabilistic Sensor Fusion for Semantic Mapping

    Berrio, Julie Stephany / Shan, Mao / Worrall, Stewart et al. | IEEE | 2022


    Metro Rail Detection Based on Vehicle-Borne LiDAR

    Zeng, Xiang / Leng, Binghan / Jiang, Guotao et al. | British Library Conference Proceedings | 2022