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SIFT, SURF and seasons: long-term outdoor localization using local features
Örebro University, Department of Technology. (Learning Systems Lab)
Örebro University, Department of Technology. (Learning Systems Lab)ORCID iD: 0000-0003-0217-9326
2007 (English)In: Proceedings of the European Conference on Mobile Robots, ECMR (2007), 2007, p. 253-258Conference paper, Published paper (Refereed)
Abstract [en]

Local feature matching has become a commonly used method to compare images. For mobile robots, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. In this paper, we address the issues of outdoor appearance-based topological localization for a mobile robot over time. Our data sets, each consisting of a large number of panoramic images, have been acquired over a period of nine months with large seasonal changes (snowcovered ground, bare trees, autumn leaves, dense foliage, etc.). Two different types of image feature algorithms, SIFT and the more recent SURF, have been used to compare the images. We show that two variants of SURF, called U-SURF and SURF-128, outperform the other algorithms in terms of accuracy and speed.

Place, publisher, year, edition, pages
2007. p. 253-258
National Category
Engineering and Technology Computer and Information Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-4263OAI: oai:DiVA.org:oru-4263DiVA, id: diva2:138562
Conference
3rd European conference on mobile robots, ECMR '07, September 19-21, Freiburg, Germany
Available from: 2007-12-13 Created: 2007-12-13 Last updated: 2018-01-13Bibliographically approved

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No full text in DiVA

Other links

http://ecmr07.informatik.uni-freiburg.de/proceedings/ECMR07_0050.pdf

Authority records BETA

Lilienthal, Achim J.

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • text
  • asciidoc
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