Publikation
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, Pages 145-156, Lecture Notes in Artificial Intelligence (LNAI), Vol. 6556, Springer, Heidelberg, 2011.
Zusammenfassung
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.