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SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments
Department of Computer Science, Örebro University, Örebro, Sweden. (AASS)
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0003-0217-9326
2010 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 58, no 2, 149-156 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we address the problem of outdoor, appearance-based topological localization, particularly over long periods of time where seasonal changes alter the appearance of the environment. We investigate a straight-forward method that relies on local image features to compare single image pairs. We rst look into which of the dominating image feature algorithms, SIFT or the more recent SURF, that is most suitable for this task. We then ne-tune our localization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result. The nal localization algorithm is applied on multiple data sets, each consisting of a large number of panoramic images, which have been acquired over a period of nine months with large seasonal changes. The nal localization rate in the single-image matching, cross-seasonal case is between 80 to 95%.

Place, publisher, year, edition, pages
Amsterdam, Netherlands: Elsevier, 2010. Vol. 58, no 2, 149-156 p.
Keyword [en]
Localization, Scene Recognition, Outdoor Environments
National Category
Computer Science
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-10273DOI: 10.1016/j.robot.2009.09.010ISI: 000275072000004Scopus ID: 2-s2.0-75149148002OAI: oai:DiVA.org:oru-10273DiVA: diva2:306578
Available from: 2010-03-30 Created: 2010-03-30 Last updated: 2017-02-14Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Language
  • de-DE
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  • nn-NB
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  • Other locale
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Output format
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