Localization is a key problem for autonomous vehicles in unmanned logistics of industrial scenes. However, due to a large amount of observation noise, the localization robustness of commonly used SLAM methods and traditional sensor configuration schemes is insufficient in dynamic industrial environments. To address the above issue, this paper proposes a system based on the truss feature and solves the key problem that the severe interference of dynamic objects such as industrial equipment, vehicles, and pedestrians causes errors in map matching results. First, the LiDAR with a hemispherical field of view is placed upwards to cover the truss feature. Then, the truss feature is effectively extracted based on the point cloud curvature for point cloud registration. After that, the wheel speed and steering angle information are fused to obtain the final poses. Considerable experiments in real dynamic environments demonstrate the superior robustness of our system, with an average localization error of less than 5.0 cm at 20 Hz.


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    Titel :

    Truss Feature Based Robust Localization Method for Vehicles in Dynamic Industrial Scene


    Beteiligte:
    Wu, Ying (Autor:in) / Yuan, Wei (Autor:in) / Wang, Bing (Autor:in) / Lian, Chaochun (Autor:in) / Yao, Yongchun (Autor:in) / Cai, Yan (Autor:in) / Yang, Ming (Autor:in)


    Erscheinungsdatum :

    2023-06-04


    Format / Umfang :

    7713970 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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