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Self-localization in non-stationary environments using omni-directional vision
Örebro University, Department of Technology. (AASS, Learning systems lab)ORCID iD: 0000-0002-2953-1564
University of Tübingen. (Dept. of Computer Science WSI-RA)
Örebro University, Department of Technology. (AASS, Learning systems lab)
2007 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 55, no 7, p. 541-551Article in journal (Refereed) Published
Abstract [en]

This paper presents an image-based approach for localization in non-static environments using local feature descriptors, and its experimental evaluation in a large, dynamic, populated environment where the time interval between the collected data sets is up to two months. By using local features together with panoramic images, robustness and invariance to large changes in the environment can be handled. Results from global place recognition with no evidence accumulation and a Monte Carlo localization method are shown. To test the approach even further, experiments were conducted with up to 90% virtual occlusion in addition to the dynamic changes in the environment

Place, publisher, year, edition, pages
Amsterdam: Elsevier , 2007. Vol. 55, no 7, p. 541-551
National Category
Engineering and Technology Computer and Information Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-5185DOI: 10.1016/j.robot.2007.02.002OAI: oai:DiVA.org:oru-5185DiVA, id: diva2:158113
Available from: 2009-01-30 Created: 2009-01-30 Last updated: 2018-01-13Bibliographically approved

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Andreasson, HenrikDuckett, Tom

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