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Stereo vision based terrain mapping for off-road autonomous navigation

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dc.contributor.author Rankin, Arturo L.
dc.contributor.author Huertas, Andres
dc.contributor.author Matthies, Larry H.
dc.date.accessioned 2014-08-07T21:38:16Z
dc.date.available 2014-08-07T21:38:16Z
dc.date.issued 2009-04-13
dc.identifier.citation SPIE Defense, Security and Sensing, Orlando, Florida, April 13-17, 2009 en_US
dc.identifier.clearanceno 09-1215
dc.identifier.uri http://hdl.handle.net/2014/44631
dc.description.abstract Successful off-road autonomous navigation by an unmanned ground vehicle (UGV) requires reliable perception and representation of natural terrain. While perception algorithms are used to detect driving hazards, terrain mapping algorithms are used to represent the detected hazards in a world model a UGV can use to plan safe paths. There are two primary ways to detect driving hazards with perception sensors mounted to a UGV: binary obstacle detection and traversability cost analysis. Binary obstacle detectors label terrain as either traversable or non-traversable, whereas, traversability cost analysis assigns a cost to driving over a discrete patch of terrain. In uncluttered environments where the non-obstacle terrain is equally traversable, binary obstacle detection is sufficient. However, in cluttered environments, some form of traversability cost analysis is necessary. The Jet Propulsion Laboratory (JPL) has explored both approaches using stereo vision systems. A set of binary detectors has been implemented that detect positive obstacles, negative obstacles, tree trunks, tree lines, excessive slope, low overhangs, and water bodies. A compact terrain map is built from each frame of stereo images. The mapping algorithm labels cells that contain obstacles as no-go regions, and encodes terrain elevation, terrain classification, terrain roughness, traversability cost, and a confidence value. The single frame maps are merged into a world map where temporal filtering is applied. In previous papers, we have described our perception algorithms that perform binary obstacle detection. In this paper, we summarize the terrain mapping capabilities that JPL has implemented during several UGV programs over the last decade and discuss some challenges to building terrain maps with stereo range data. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2009 en_US
dc.subject passive perception en_US
dc.title Stereo vision based terrain mapping for off-road autonomous navigation en_US
dc.type Preprint en_US


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