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Proceedings Paper

Remote imagery for unmanned ground vehicles: the future of path planning for ground robotics
Author(s): Philip A. Frederick; Bernard L. Theisen; Derek Ward
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Paper Abstract

Remote Imagery for Unmanned Ground Vehicles (RIUGV) uses a combination of high-resolution multi-spectral satellite imagery and advanced commercial off-the-self (COTS) object-oriented image processing software to provide automated terrain feature extraction and classification. This information, along with elevation data, infrared imagery, a vehicle mobility model and various meta-data (local weather reports, Zobler Soil map, etc...), is fed into automated path planning software to provide a stand-alone ability to generate rapidly updateable dynamic mobility maps for Manned or Unmanned Ground Vehicles (MGVs or UGVs). These polygon based mobility maps can reside on an individual platform or a tactical network. When new information is available, change files are generated and ingested into existing mobility maps based on user selected criteria. Bandwidth concerns are mitigated by the use of shape files for the representation of the data (e.g. each object in the scene is represented by a shape file and thus can be transmitted individually). User input (desired level of stealth, required time of arrival, etc...) determines the priority in which objects are tagged for updates. This paper will also discuss the planned July 2006 field experiment.

Paper Details

Date Published: 2 October 2006
PDF: 14 pages
Proc. SPIE 6384, Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision, 638405 (2 October 2006); doi: 10.1117/12.685390
Show Author Affiliations
Philip A. Frederick, U.S. Army Tank Automotive Research, Development, and Engineering Ctr. (United States)
Bernard L. Theisen, U.S. Army Tank Automotive Research, Development, and Engineering Ctr. (United States)
Derek Ward, Lockheed Martin Missiles and Fire Control (United States)


Published in SPIE Proceedings Vol. 6384:
Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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