DFKI-LT - Optimizing Particle Filter Parameters for Self-Localization

Armin Burchardt, Tim Laue, Thomas Röfer
Optimizing Particle Filter Parameters for Self-Localization
in: J. Ruiz-del-Solar, E. Chown, P.G. Ploeger (eds.):
RoboCup 2010: Robot Soccer World Cup XIV volume 6556,
Lecture Notes in Artificial Intelligence, Pages 145-156, Singapore, Singapore, 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.
 
Files: BibTeX, RC-Burchardt-etal-11.pdf