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Tackling the Premature Convergence Problem in Monte-Carlo Localization
Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands. (Artificial Intelligence)
Univesity of Amsterdam, The Netherlands.
2009 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 0921-8830, Vol. 57, no 11, 1107-1118 p.Article in journal (Refereed) Published
Abstract [en]

Monte-Carlo localization uses particle filtering to estimate the position of the robot. The method is known to suffer from the loss of potential positions when there is ambiguity present in the environment. Since many indoor environments are highly symmetric, this problem of premature convergence is problematic for indoor robot navigation. It is, however, rarely studied in particle filters. We introduce a number of so-called niching methods used in genetic algorithms, and implement them on a particle filter for Monte-Carlo localization. The experiments show a significant improvement in the diversity maintaining performance of the particle filter.

Place, publisher, year, edition, pages
Amsterdam, The Netherlands: Elsevier , 2009. Vol. 57, no 11, 1107-1118 p.
Keyword [en]
Particle Filters, Particle Depletion, Premature Convergence, Niching Methods
National Category
Robotics Signal Processing
URN: urn:nbn:se:kth:diva-47170DOI: 10.1016/j.robot.2009.07.003ISI: 000272526600006OAI: diva2:454632
NOTICE: this is the author’s version of a work that was accepted for publication in Robotics and Autonomous Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Robotics and Autonomous System, VOL 57, ISSUE 11, 17 July 2009. DOI:10.1016/j.robot.2009.07.003 QC 20111108Available from: 2011-11-08 Created: 2011-11-07 Last updated: 2011-11-08Bibliographically approved

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