Segvic07a
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, Juin 2007.
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Résumé
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
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@InProceedings{Segvic07a,
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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