This paper investigates real-time road detection for an unmanned ground vehicle (UGV) to navigate in a campus environment. A novel vision system with two monocular cameras at different heights and angles is utilized to accomplish both road region detection and direction estimation tasks simultaneously. An Adaboost-based classifier is used in the road region detection in order to handle the road surface's diversity, and a vanishing point tracking technique is used to estimate the road direction. To describe the unstructured road spaces with an accurate model, a RANSAC Spline Fitting algorithm is adopted to delineate the road borders based on the data fusion results from vision and lasers. Extensive experiments are carried out by using a real UGV platform in a campus environment and results show the feasibility and performance of the proposed approach.


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    Titel :

    Real-time road detection and description for robot navigation in an unstructured campus environment


    Beteiligte:
    Wenhao Xu (Autor:in) / Yan Zhuang (Autor:in) / Huosheng Hu (Autor:in) / Yiwen Zhao (Autor:in)


    Erscheinungsdatum :

    01.06.2014


    Format / Umfang :

    2060829 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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