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Terrain Classification and Classifier Fusion for Planetary Exploration Rovers
Massachusetts Institute of Technology, Department of Mechanical Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, United States.
Massachusetts Institute of Technology, Department of Mechanical Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, United States.
Massachusetts Institute of Technology, Department of Mechanical Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, United States.
2007 (English)In: Aerospace Conference, 2007 IEEE, Piscataway: IEEE Press, 2007, 1-11 p.Conference paper, Published paper (Refereed)
Abstract [en]

Knowledge of the physical properties of terrain surrounding a planetary exploration rover can be used to allow a rover system to fully exploit its mobility capabilities. Here a study of multi-sensor terrain classification for planetary rovers in Mars and Mars-like environments is presented. Two classification algorithms for color, texture, and range features are presented based on maximum likelihood estimation and support vector machines. In addition, a classification method based on vibration features derived from rover wheel-terrain interaction is briefly described. Two techniques for merging the results of these "low-level" classifiers are presented that rely on Bayesian fusion and meta-classifier fusion. The performance of these algorithms is studied using images from NASA's Mars Exploration Rover mission and through experiments on a four-wheeled test-bed rover operating in Mars-analog terrain. It is shown that accurate terrain classification can be achieved via classifier fusion from visual and tactile features.

Place, publisher, year, edition, pages
Piscataway: IEEE Press, 2007. 1-11 p.
Series
IEEE Aerospace Conference. Proceedings, ISSN 1095-323X
Keyword [en]
Bayesian methods, Classification algorithms, Layout, Mars, Maximum likelihood estimation, Merging, Support vector machine classification, Support vector machines, Testing, Wheels, Bayes methods, Image classification, Image colour analysis, Image texture, Maximum likelihood estimation, Planetary rovers, Sensor fusion, Support vector machines, Bayesian fusion, Mars, NASA Mars Exploration Rover mission, Classifier fusion, Four-wheeled test-bed rover, Maximum likelihood estimation, Meta-classifier fusion, Multi-sensor terrain classification, Planetary exploration rovers, Rover wheel-terrain interaction, Support vector machines, Vibration features
National Category
Robotics
Identifiers
URN: urn:nbn:se:hh:diva-20820DOI: 10.1109/AERO.2007.352692ISI: 000251235302085Scopus ID: 2-s2.0-34548731283ISBN: 1-4244-0525-4 ISBN: 978-1-4244-0524-4 OAI: oai:DiVA.org:hh-20820DiVA: diva2:586713
Conference
2007 IEEE Aerospace Conference, Big Sky, MT, MAR 03-10, 2007
Available from: 2013-01-12 Created: 2013-01-12 Last updated: 2013-02-18Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
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More styles
Language
  • de-DE
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Output format
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