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S. Segvic, A. Remazeilles, A. Diosi, F. Chaumette. Large scale vision based navigation without an accurate global reconstruction. In IEEE Int. Conf. on Computer Vision and Pattern Recognition, CVPR'07, Pages 1-8, Minneapolis, Minnesota, June 2007.

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Autonomous cars will likely play an important role in the future. A vision system designed to support outdoor navigation for such vehicles has to deal with large dynamic environments, changing imaging conditions, and temporary occlusions by other moving objects. This paper presents a novel appearance-based navigation framework relying on a single perspective vision sensor, which is aimed towards resolving of the above issues. The solution is based on a hierarchical environment representation created during a teaching stage, when the robot is controlled by a human operator. At the top level, the representation contains a graph of key-images with extracted 2D features enabling a robust navigation by visual servoing. The information stored at the bottom level enables to efficiently predict the locations of the features which are currently not visible, and eventually (re-)start their tracking. The outstanding property of the proposed framework is that it enables robust and scalable navigation without requiring a globally consistent map, even in interconnected environments. This result has been confirmed by realistic off-line experiments and successful real-time navigation trials in public urban areas


Francois Chaumette

BibTex Reference

   Author = {Segvic, S. and Remazeilles, A. and Diosi, A. and Chaumette, F.},
   Title = {Large scale vision based navigation without an accurate global reconstruction},
   BookTitle = {IEEE Int. Conf. on Computer Vision and Pattern Recognition, CVPR'07},
   Pages = {1--8},
   Address = {Minneapolis, Minnesota},
   Month = {June},
   Year = {2007}

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