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Visual Detection of Novel Terrain via Two-Class Classification
MIT.
MIT.
2009 (engelsk)Inngår i: Proceedings of the 24th Annual ACM: Symposium on Applied Computing 2009, New York: ACM Press, 2009, s. 1145-1150Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
New York: ACM Press, 2009. s. 1145-1150
Emneord [en]
Image classification, Machine vision, Robot sensing systems, Terrain mapping
HSV kategori
Identifikatorer
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, id: diva2:586870
Konferanse
24th Annual ACM Symposium on Applied Computing, SAC 2009, Honolulu, HI, 8-12 March, 2009
Tilgjengelig fra: 2013-01-13 Laget: 2013-01-13 Sist oppdatert: 2018-03-22bibliografisk kontrollert

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