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Fast, on-line learning of globally consistent maps
Örebro University, Department of Technology. (Learning Systems Lab)
University of Manchester. (Department of Computer Science)
University of Manchester. (Department of Computer Science)
2002 (English)In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 12, no 3, p. 287-300Article in journal (Refereed) Published
Abstract [en]

To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically consistent maps using only local metric information. The algorithm works by using a relaxation technique to minimise an energy function over many small steps. The approach differs from previous work in that it is computationally cheap, easy to implement and is proven to converge to a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot.

Place, publisher, year, edition, pages
2002. Vol. 12, no 3, p. 287-300
Keywords [en]
simultaneous localization and mapping, concurrent map-building and self-localization, relaxation algorithm, Gibbs sampling, learning and adaptation
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-3552DOI: 10.1023/A:1015269615729OAI: oai:DiVA.org:oru-3552DiVA, id: diva2:137849
Available from: 2007-07-22 Created: 2007-07-22 Last updated: 2018-01-13Bibliographically approved

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Duckett, Tom

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  • Other locale
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Output format
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