In this paper, we propose an illumination-invariant nonparametric model for urban road detection based on a monocular camera and a single-line LIDAR sensor. With the monocular camera, we propose a new shadow removal method to obtain an illumination-invariant image representation. Consequently, we can accurately locate the road vanishing point after removing the adverse shadowy effect. With the constraint of the detected vanishing point, we propose a Dijkstra-based method to compute a minimum-cost map, where the minimum-cost path from the vanishing point to any other pixel can be found. With the single line LIDAR sensor, we can locate a few potential curb points in the image bottom region, and thus we can obtain several corresponding minimum-cost paths that originate from the vanishing point to the curb points. Thereafter, two most likely road borders can be found from these paths, respectively. Our learning-free method has been tested on over 4000 images of the KITTI-Odometry Dataset [A. Geiger, P. Lenz, and R.Urtasun, “Are we ready for autonomous driving? The KITTI vision benchmark suite,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2012, pp. 3354–3361.] and the Oxford Robotcar Dataset [W. Maddern, G. Pascoe, C. Linegar, and P. Newman, “1 year, 1000 km: The Oxford robotcar dataset,” Int. J. Robot. Res., vol. 36, no. 1, pp. 3–15, 2017.]. It works accurately on a variety of road scenes and is competitive compared to state-of-the-art deep learning methods that need extensive training data.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An Illumination-Invariant Nonparametric Model for Urban Road Detection


    Contributors:
    Su, Yingna (author) / Gao, Yicheng (author) / Zhang, Yigong (author) / Alvarez, Jose M. (author) / Yang, Jian (author) / Kong, Hui (author)

    Published in:

    Publication date :

    2019-03-01


    Size :

    4303092 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Illumination invariant road detection based on learning method

    Bongjoe Kim, / Jongin Son, / Kwanghoon Sohn, | IEEE | 2011


    A novel illumination-invariant lane detection system

    Kortli, Yassin / Marzougui, Mehrez / Bouallegue, Belgacem et al. | IEEE | 2017


    Illumination invariant background extraction

    Durucan, E. / Snoeckx, J. / Weilenmann, Y. | IEEE | 1999


    Enhanced illumination-invariant imaging

    HERMAN DAVID MICHAEL / VAIDYA AKSHAY / SANDERS LARRY | European Patent Office | 2024

    Free access

    Enhanced Illumination-Invariant Imaging

    HERMAN DAVID MICHAEL / VAIDYA AKSHAY / SANDERS LARRY | European Patent Office | 2023

    Free access