DocumentCode :
2572879
Title :
Rough Terrain Reconstruction for Rover Motion Planning
Author :
Gingras, David ; Lamarche, Tom ; Bedwani, Jean-Luc ; Dupuis, Erick
Author_Institution :
Space Exploration, Canadian Space Agency, St. Hubert, QC, Canada
fYear :
2010
fDate :
May 31 2010-June 2 2010
Firstpage :
191
Lastpage :
198
Abstract :
A two-step approach is presented to generate a 3D navigable terrain model for robots operating in natural and uneven environment. First an unstructured surface is built from a 360 degrees field of view LIDAR scan. Second the reconstructed surface is analyzed and the navigable space is extracted to keep only the safe area as a compressed irregular triangular mesh. The resulting mesh is a compact terrain representation and allows point-robot assumption for further motion planning tasks. The proposed algorithm has been validated using a large database containing 688 LIDAR scans collected on an outdoor rough terrain. The mesh simplification error was evaluated using the approximation of Hausdorff distance. In average, for a compression level of 93.5%, the error was of the order of 0.5 cm. This terrain modeler was deployed on a rover controlled from the International Space Station (ISS) during the Avatar Explore Space Mission carried out by the Canadian Space Agency in 2009.
Keywords :
approximation theory; image reconstruction; image representation; mesh generation; mobile robots; path planning; robot vision; 3D terrain model; Avatar Explore Space Mission; Hausdorff distance approximation; International Space Station; LIDAR scan; compact terrain representation; compressed irregular triangular mesh; mesh simplification error; rough terrain reconstruction; rover motion planning; Avatars; Databases; International Space Station; Laser radar; Motion planning; Orbital robotics; Rough surfaces; Space missions; Surface reconstruction; Surface roughness; irregular triangular mesh; lidar; rover; space exploration; terrain reconstruction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Robot Vision (CRV), 2010 Canadian Conference on
Conference_Location :
Ottawa, ON
Print_ISBN :
978-1-4244-6963-5
Type :
conf
DOI :
10.1109/CRV.2010.32
Filename :
5479187
Link To Document :
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