This work studies a supervised learning method using 3D LiDAR data for autonomous driving applications. A system of semantic segmentation, including range image segmentation, sample generation, track-level annotation and supervised learning, is developed. The formation and content of a data sample is studied intensively to address the specialty of 3D LiDAR data, which can be represented at a Cartesian or a 2D polar coordinate system, and composed of a segment as the foreground and/or the neighborhood points as the background. A CNN-based classifier is trained to map a given sample to an object label. Qualitative and quantitative experiments show that the background information and multiple feature map fusion significantly improve the performance of the classifier.


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    Title :

    Supervised Learning for Semantic Segmentation of 3D LiDAR Data


    Contributors:
    Mei, Jilin (author) / Chen, Jiayu (author) / Yao, Wen (author) / Zhao, Xijun (author) / Zhao, Huijing (author)


    Publication date :

    2019-06-01


    Size :

    3156698 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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