Obstacle avoidance is one of the most important challenges for mobile robots as well as future vision based driver assistance systems. This task requires a precise extraction of depth and the robust and fast detection of moving objects. In order to reach these goals, this paper considers vision as a process in space and time. It presents a powerful fusion of depth and motion information for image sequences taken from a moving observer. 3D-position and 3D-motion for a large number of image points are estimated simultaneously by means of Kalman-Filters. There is no need of prior error-prone segmentation. Thus, one gets a rich 6D representation that allows the detection of moving obstacles even in the presence of partial occlusion of foreground or background.


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    Titel :

    6D-vision: fusion of stereo and motion for robust environment perception


    Beteiligte:
    Franke, Uwe (Autor:in) / Rabe, Clemens (Autor:in) / Badino, Hernan (Autor:in) / Gehrig, Stefan (Autor:in)


    Erscheinungsdatum :

    2005


    Format / Umfang :

    8 Seiten, 5 Bilder, 11 Quellen





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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