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Visual Detection of Novel Terrain via Two-Class Classification
MIT.
MIT.
2009 (English)In: Proceedings of the 24th Annual ACM: Symposium on Applied Computing 2009, New York: ACM Press, 2009, 1145-1150 p.Conference paper, Published paper (Refereed)
Abstract [en]

Remote sensing of terrain characteristics is an important component for autonomous operation of mobile robots in natural terrain. Often this involves classification of terrain into one of a set of a priori known terrain classes. Situations can frequently arise, however, where an autonomous robot encounters a terrain class that does not belong to one of these known classes. This paper proposes an approach for visual detection of novel terrain based on a two-class support vector machine (SVM) for situations when known terrain classes can be confidently associated with only a subset of the training data. Experimental results from a four-wheeled mobile robot in Mars analog terrain demonstrate the effectiveness of this approach. Copyright 2009 ACM.

Place, publisher, year, edition, pages
New York: ACM Press, 2009. 1145-1150 p.
Keyword [en]
Image classification, Machine vision, Robot sensing systems, Terrain mapping
National Category
Robotics
Identifiers
URN: urn:nbn:se:hh:diva-20858DOI: 10.1145/1529282.1529537Scopus ID: 2-s2.0-72949122547ISBN: 978-160558166-8 OAI: oai:DiVA.org:hh-20858DiVA: diva2:586870
Conference
24th Annual ACM Symposium on Applied Computing, SAC 2009, Honolulu, HI, 8-12 March, 2009
Available from: 2013-01-13 Created: 2013-01-13 Last updated: 2013-02-20Bibliographically approved

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Total: 77 hits
CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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Output format
  • html
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  • asciidoc
  • rtf