The effective detection of curbs is fundamental and crucial for the navigation of a self-driving car. This paper presents a real-time curb detection method that automatically segments the road and detects its curbs using a 3D-LiDAR sensor. The point cloud data of the sensor are first processed to distinguish on-road and off-road areas. A sliding-beam method is then proposed to segment the road by using the off-road data. A curb-detection method is finally applied to obtain the position of curbs for each road segments. The proposed method is tested on the data sets acquired from the self-driving car of laboratory of VeCaN at Tongji University. Off-line experiments demonstrate the accuracy and robustness of the proposed method, i.e., the average recall, precision and their harmonic mean are all over 80%. Online experiments demonstrate the real-time capability for autonomous driving as the average processing time for each frame is only around 12 ms.


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    Titel :

    Road-Segmentation-Based Curb Detection Method for Self-Driving via a 3D-LiDAR Sensor


    Beteiligte:
    Zhang, Yihuan (Autor:in) / Wang, Jun (Autor:in) / Wang, Xiaonian (Autor:in) / Dolan, John M. (Autor:in)


    Erscheinungsdatum :

    01.12.2018


    Format / Umfang :

    3571184 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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