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Localization for mobile robots using panoramic vision, local features and particle filter
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)
2005 (English)In: Proceedings of the 2005 IEEE International Converence on Robotics and Automation: ICRA - 2005, 2005, 3348-3353 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present a vision-based approach to self-localization that uses a novel scheme to integrate featurebased matching of panoramic images with Monte Carlo localization. A specially modified version of Lowe’s SIFT algorithm is used to match features extracted from local interest points in the image, rather than using global features calculated from the whole image. Experiments conducted in a large, populated indoor environment (up to 5 persons visible) over a period of several months demonstrate the robustness of the approach, including kidnapping and occlusion of up to 90% of the robot’s field of view.

Place, publisher, year, edition, pages
2005. 3348-3353 p.
National Category
Computer Science
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-5183DOI: 10.1109/ROBOT.2005.1570627OAI: oai:DiVA.org:oru-5183DiVA: diva2:158109
Conference
IEEE International Converence on Robotics and Automation, ICRA-2005, Barcelona, Spain, 18-22 April 2005
Available from: 2009-01-30 Created: 2009-01-30 Last updated: 2017-10-18Bibliographically approved

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CiteExportLink to record
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Citation style
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