Localization is an essential problem in autonomous navigation of self-driving cars. We present a monocular vision based approach for localization in urban environments using road markings. We utilize road markings as landmarks instead of traditional visual features (e.g. SIFT) to tackle the localization problem because road markings are more robust against changes in perspective, illumination, and across time. Specifically, we employ Chamfer matching to register edges of road markings against a lightweight 3D map where road markings are represented as a set of sparse points. By only matching geometry of road markings, our localization algorithm further gains robustness against photometric appearance changes in the environment. We take vehicle odometry and epipolar geometry constraints into account and formulate a non-linear optimization problem to estimate the 6 DoF camera pose. We evaluate the proposed method on data collected in the real world. Experimental results show that our method achieves sub-meter localization errors in areas with sufficient road markings.


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    Title :

    Monocular localization in urban environments using road markings


    Contributors:
    Lu, Yan (author) / Huang, Jiawei (author) / Chen, Yi-Ting (author) / Heisele, Bernd (author)


    Publication date :

    2017-06-01


    Size :

    1884376 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Monocular Localization in Urban Environments Using Road Markings

    Lu, Yan / Huang, Jiawei / Chen, Yi-Ting et al. | British Library Conference Proceedings | 2017


    Vehicle localization using road markings

    Wu, Tao / Ranganathan, Ananth | IEEE | 2013


    VEHICLE LOCALIZATION USING ROAD MARKINGS

    Wu, T. / Ranganathan, A. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2013