Abstract
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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Franke, U., Rabe, C., Badino, H., Gehrig, S. (2005). 6D-Vision: Fusion of Stereo and Motion for Robust Environment Perception. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_27
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DOI: https://doi.org/10.1007/11550518_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28703-2
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