Uwe Franke, Clemens Rabe, Hernán Badino, and Stefan K. GehrigAbstractObstacle 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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