6D-Vision: Fusion of Stereo and Motion for Robust Environment Perception

  • Uwe Franke
  • Clemens Rabe
  • Hernán Badino
  • Stefan Gehrig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3663)


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.


Kalman Filter Image Point Obstacle Avoidance Stereo Vision Inertial Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Uwe Franke
    • 1
  • Clemens Rabe
    • 1
  • Hernán Badino
    • 1
  • Stefan Gehrig
    • 1
  1. 1.DaimlerChrysler AGStuttgartGermany

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