Optimizing Particle Filter Parameters for Self-Localization

Armin Burchardt, Tim Laue, Thomas Röfer

In: Javier Ruiz-del-Solar , Eric Chown , Paul G. Ploeger (Hrsg.). RoboCup 2010: Robot Soccer World Cup XIV. RoboCup International Symposium (RoboCup-2010) June 19-25 Singapore Singapore Seiten 145-156 Lecture Notes in Artificial Intelligence (LNAI) 6556 Springer Heidelberg 2011.


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.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence