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Publication type: Article in Proceedings
Author: Armin Burchardt, Tim Laue, Thomas Röfer
Editor: Javier Ruiz-del-Solar, Eric Chown, Paul G. Plöger
Title: Optimizing Particle Filter Parameters for Self-Localization
Book / Collection title: RoboCup 2010: Robot Soccer World Cup XIV
Page(s): 145 – 156
Series: Lecture Notes in Artificial Intelligence
Year published: 2011
Publisher: Springer, Heidelberg
Abstract: Particle filter-based approaches have proven to be capable of efficiently solving the self-localization problem in RoboCup scenarios and are therefore applied by many participating teams. Nevertheless, they require a proper parametrization - for sensor models and dynamic models as well as for the configuration of the algorithm - to operate reliably. In this paper, we present an approach for optimizing all relevant parameters by using the Particle Swarm Optimization algorithm. The approach has been applied to the self-localization component of a Standard Platform League team and shown to be capable of finding a parameter set that leads to more precise position estimates than the previously used hand-tuned parametrization.
PDF Version: http://www.informatik.uni-bremen.de/kogrob/papers/RC-Burchardt-etal-11.pdf
Status: Reviewed
Last updated: 06. 11. 2013

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