In this paper, we propose a novel LiDAR extrinsic parameter self-calibration method, DyLESC. With the driving trajectories obtained from GPS/IMU, a set of point cloud maps can be established by accumulating the LiDAR frames. The method introduces a function to evaluate the blurriness of the point cloud maps, which involves both geometrical features and point-number factors. By minimizing the blurriness of the point cloud maps, LiDAR extrinsic parameters are automatically calibrated. The method limits the frame number in a point cloud map to avoid the accumulated bumpy errors of GPS/IMU. For improving the robustness, the moving objects are removed from the LiDAR frames. We test the method both in a simulation environment and in the real world. The experiment result shows that DyLESC can accurately calibrate the LiDAR extrinsic parameters.


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    Titel :

    DyLESC: A Dynamic LiDAR Extrinsic Self-Calibration Method for Intelligent Driving Vehicles


    Beteiligte:
    Jie, Haoxiang (Autor:in) / Zhou, Yibo (Autor:in) / Ning, Zuotao (Autor:in) / Zhao, Qixi (Autor:in) / Liu, Lifeng (Autor:in) / Hu, Jun (Autor:in)


    Erscheinungsdatum :

    2022-11-11


    Format / Umfang :

    1620428 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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