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Proceedings Paper

Detecting water hazards for autonomous off-road navigation
Author(s): Larry Henry Matthies; Paolo Bellutta; Mike McHenry
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Paper Abstract

Detecting water hazards for autonomous, off-road navigation of unmanned ground vehicles is a largely unexplored problem. In this paper, we catalog environmental variables that affect the difficulty of this problem, including day vs. night operation, whether the water reflects sky or other terrain features, the size of the water body, and other factors. We briefly survey sensors that are applicable to detecting water hazards in each of these conditions. We then present analyses and results for water detection for four specific sensor cases: (1) using color image classification to recognize sky reflections in water during the day, (2) using ladar to detect the presense of water bodies and to measure their depth, (3) using short-wave infrared (SWIR) imagery to detect water bodies, as well as snow and ice, and (4) using mid-wave infrared (MWIR) imagery to recognize water bodies at night. For color imagery, we demonstrate solid results with a classifier that runs at nearly video rate on a 433 MHz processor. For ladar, we present a detailed propagation analysis that shows the limits of water body detection and depth estimation as a function of lookahead distance, water depth, and ladar wavelength. For SWIR and MWIR, we present sample imagery from a variety of data collections that illustrate the potential of these sensors. These results demonstrate significant progress on this problem.

Paper Details

Date Published: 30 September 2003
PDF: 12 pages
Proc. SPIE 5083, Unmanned Ground Vehicle Technology V, (30 September 2003); doi: 10.1117/12.496942
Show Author Affiliations
Larry Henry Matthies, Jet Propulsion Lab. (United States)
Paolo Bellutta, Jet Propulsion Lab. (United States)
Mike McHenry, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 5083:
Unmanned Ground Vehicle Technology V
Grant R. Gerhart; Charles M. Shoemaker; Douglas W. Gage, Editor(s)

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