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Optimizing Particle Filter Parameters for Self-localization

  • Armin Burchardt
  • Tim Laue
  • Thomas Röfer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6556)

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.

Keywords

Particle Swarm Optimization Humanoid Robot Benchmark Function Sensor Model Goal Post 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Armin Burchardt
    • 1
  • Tim Laue
    • 2
  • Thomas Röfer
    • 2
  1. 1.Fachbereich 3 – Mathematik und InformatikUniversität BremenBremenGermany
  2. 2.Deutsches Forschungszentrum für Künstliche IntelligenzSichere Kognitive SystemeBremenGermany

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