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Localization of mobile robots with omnidirectional vision using particle filter and iterative SIFT
University of Tubingen. (Department of Computer Science)
Örebro University, Department of Technology. (Learning Systems Lab)ORCID iD: 0000-0002-2953-1564
University of Tubingen. (Dept. of Computer Architecture)
Örebro University, Department of Technology. (Learning Systems Lab)
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2005 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

The Scale Invariant Feature Transform, SIFT, has been successfully applied to robot localization. Still, the number of features extracted with this approach is immense, especially when dealing with omnidirectional vision. In this work, we propose a new approach that reduces the number of features generated by SIFT as well as their extraction and matching time. With the help of a particle filter, we demonstrate that we can still localize the mobile robot accurately with a lower number of features

Place, publisher, year, edition, pages
2005.
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-3968OAI: oai:DiVA.org:oru-3968DiVA, id: diva2:138267
Conference
2nd European Conference on Mobile Robots, ECMR 2005, Ancona, Italy
Available from: 2007-08-29 Created: 2007-08-29 Last updated: 2018-01-13Bibliographically approved

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Andreasson, HenrikDuckett, Tom

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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  • Other locale
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