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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    Title :

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


    Contributors:


    Publication date :

    2005


    Size :

    8 Seiten, 5 Bilder, 11 Quellen





    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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