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A Minimalistic Approach to Appearance-Based Visual SLAM
Örebro University, Department of Technology. (AASS, Learning systems lab)ORCID iD: 0000-0002-2953-1564
University of Lincoln, University of Lincoln, UK. (Dept. of Computer Science)
(AASS)ORCID iD: 0000-0003-0217-9326
2008 (English)In: IEEE Transactions on Robotics, ISSN 1552-3098, Vol. 24, no 5, p. 991-1001Article in journal (Refereed) Published
Abstract [en]

This paper presents a vision-based approach to SLAM in indoor / outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omni-directional vision sensor, a novel method is introduced based on the relative similarity of neighbouring images. This new method does not require determining distances to image features using multiple view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle qualitatively different environments (without modification of the parameters), that it can cope with violations of the “flat floor assumption” to some degree, and that it scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g. for solving the multi-robot SLAM problem with unknown initial poses.

Place, publisher, year, edition, pages
New York, NY, USA: IEEE, 2008. Vol. 24, no 5, p. 991-1001
Keywords [en]
Omnidirectional vision, simultaneous localization and mapping (SLAM)
National Category
Engineering and Technology Computer and Information Sciences Computer Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-5186DOI: 10.1109/TRO.2008.2004642ISI: 000260865400007Scopus ID: 2-s2.0-56049108105OAI: oai:DiVA.org:oru-5186DiVA, id: diva2:158115
Available from: 2009-01-30 Created: 2009-01-30 Last updated: 2018-06-18Bibliographically approved

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Andreasson, HenrikLilienthal, Achim J.

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