This paper proposes a reactive obstacle avoidance approach based solely on image data from a monocular camera stream. By clustering and analyzing the optical flow, this approach is able to identify potential collisions with dynamic obstacles. Epipolar geometry is exploited to derive velocity commands that ensure a collision-free path for a highly maneuverable autonomous vehicle via a real-time optimizer. First, the underlying image processing and optimization principles are explained in detail, before simulation results show the general feasibility of the approach. Finally, real-world tests with the ROboMObil, the German Aerospace Center's robotic electric vehicle, are provided to demonstrate its applicability.


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    Title :

    Reactive Obstacle Avoidance for Highly Maneuverable Vehicles Based on a Two-Stage Optical Flow Clustering




    Publication date :

    2017




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.84 / 55.24 / 55.84 Straßenverkehr / 55.24 Fahrzeugführung, Fahrtechnik




    Reactive Obstacle Avoidance for Highly Maneuverable Vehicles Based on a Two-Stage Optical Flow Clustering

    Schaub, Alexander / Baumgartner, Daniel / Burschka, Darius | IEEE | 2017


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