%0 Conference Proceedings
%F Segvic07a
%A Segvic, S.
%A Remazeilles, A.
%A Diosi, A.
%A Chaumette, F.
%T Large scale vision based navigation without an accurate global reconstruction
%B IEEE Int. Conf. on Computer Vision and Pattern Recognition, CVPR'07
%P 1-8
%C Minneapolis, Minnesota
%X 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
%U http://rainbow-doc.irisa.fr/pdf/2007_cvpr_segvic.pdf
%U http://dx.doi.org/10.1109/CVPR.2007.383025
%8 June
%D 2007