Precisely localizing a vehicle in the GNSS-denied urban area is crucial for autonomous driving. The occupancy grid-based 2D LiDAR SLAM methods scale poorly to outdoor road scenarios, while the 3D point cloud-based LiDAR SLAM methods suffer from huge computation and storage costs. Aiming at the precise real-time LiDAR SLAM for both indoor and outdoor, this paper proposed a direct 2.5D heightmap-based SLAM system. This system extended our previously proposed DLO (the direct 2.5D LiDAR odometry) method by introducing the 2.5D segment features for efficient loop closure detection. We experimented our SLAM method on the KITTI datasets and shown it superior performance compared with the existing LiDAR SLAM methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    DL-SLAM: Direct 2.5D LiDAR SLAM for Autonomous Driving


    Contributors:
    Li, Jun (author) / Zhao, Junqiao (author) / Kang, Yuchen (author) / He, Xudong (author) / Ye, Chen (author) / Sun, Lu (author)


    Publication date :

    2019-06-01


    Size :

    4188603 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    DL-SLAM: DIRECT 2.5D LIDAR SLAM FOR AUTONOMOUS DRIVING

    Li, Jun / Zhao, Junqiao / Kang, Yuchen et al. | British Library Conference Proceedings | 2019


    SLAM in Autonomous Driving

    Ren, Jianfeng / Xia, Dong | Springer Verlag | 2023


    AGPC-SLAM: Absolute Ground Plane Constrained 3D Lidar SLAM

    Weisong Wen / Li-Ta Hsu | DOAJ | 2022

    Free access

    Lidar-based SLAM and autonomous navigation for forestry quadrotors

    Hu, Xuejun / Wang, Meishan / Qian, Chenghao et al. | IEEE | 2018


    D3VIL-SLAM: 3D Visual Inertial LiDAR SLAM for Outdoor Environments

    Frosi, Matteo / Matteucci, Matteo | IEEE | 2023