Real-time hierarchical stereo Visual SLAM in large-scale environments |
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| Authors: | David Schleicher Luis M. Bergasa Manuel Ocaña Rafael Barea Elena López |
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| Affiliation: | 1. Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Nienburger Str. 1, D-30167 Hannover, Germany;2. Autonomous Vision Group, Max Planck Institute for Intelligent Systems, Spemannstr. 41, D-72076 Tübingen, Germany;3. Computer Vision and Geometry Lab, ETH Zürich, Universitätstrasse 6, CH-8092 Zürich, Switzerland;1. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada H3G 1T7;2. The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada H3G 1T7;1. King Saud University, College of Computer and Information Sciences, Computer Science Department, P.O Box 51178, Riyadh 11548, Saudi Arabia;2. CECS Dept. University of Louisville, Louisville, KY 40292, USA;1. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada H3G 1T7;2. The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada H3G 1T7;3. Département d''Informatique, Université de Sherbrooke, Sherbrooke, Canada QC J1K 2R1 |
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| Abstract: | In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by a cheap wide-angle stereo camera. Our approach divides the whole map into local sub-maps identified by the so-called fingerprints (vehicle poses). At the sub-map level (low level SLAM), 3D sequential mapping of natural landmarks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (high level SLAM) based on fingerprints has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep the local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. Some experimental results for different large-scale outdoor environments are presented, showing an almost constant processing time. |
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