Road detection is an important task in autonomous navigation systems. In this paper, we propose a road detection framework induced by the inverse depth of LiDAR's point cloud. This framework is a fusion of a 3-D LiDAR and a monocular camera, where the 3-D point cloud of LiDAR is projected onto the camera's image frame, to exploit both range and color information. For the same road detection task, we propose an inverse-depth aware fully convolutional neural network based on image information and a line scanning strategy based on an inverse-depth histogram of LiDAR's point cloud. Finally, a conditional random field fusion method integrates the two road detection results. Our method is evaluated on KITTI-Road benchmark. Experiments demonstrate that our method achieves the state-of-the-art performance in road detection among all the referable methods that have ever reported their results on the KITTI-Road benchmark.


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    Title :

    3-D LiDAR + Monocular Camera: An Inverse-Depth-Induced Fusion Framework for Urban Road Detection


    Contributors:
    Gu, Shuo (author) / Lu, Tao (author) / Zhang, Yigong (author) / Alvarez, Jose M. (author) / Yang, Jian (author) / Kong, Hui (author)

    Published in:

    Publication date :

    2018-09-01


    Size :

    4361398 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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