We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application. A specific deep learning framework is designed to deal with the ambiguous area, which is one of the main challenges in the off-road environment. To reduce the considerable demand for human-annotated data for network training, we utilize the information from vast quantities of vehicle paths and auto-generated obstacle labels. Using these auto-generated annotations, the proposed network can be trained using weakly supervised or semi-supervised methods, which can achieve better performance with fewer human annotations. The experiments on our dataset illustrate the reasonability of our framework and the validity of our weakly and semi-supervised methods.


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    Title :

    Off-Road Drivable Area Extraction Using 3D LiDAR Data


    Contributors:
    Gao, Biao (author) / Xu, Anran (author) / Pan, Yancheng (author) / Zhao, Xijun (author) / Yao, Wen (author) / Zhao, Huijing (author)


    Publication date :

    2019-06-01


    Size :

    3557980 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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