A Comparative Analysis of Particle Filter Based Localization Methods

  • Luca Marchetti
  • Giorgio Grisetti
  • Luca Iocchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4434)


Self-localization is a deeply investigated field in mobile robotics, and many effective solutions have been proposed. In this context, Monte Carlo Localization (MCL) is one of the most popular approaches, and represents a good tradeoff between robustness and accuracy. The basic underlying principle of this family of approaches is using a Particle Filter for tracking a probability distribution of the possible robot poses.

Whereas the general particle filter framework specifies the sequence of operations that should be performed, it leaves open several choices including the observation and the motion model and it does not directly address the problem of robot kidnapping.

The goal of this paper is to provide a systematic analysis of Particle Filter Localization methods, considering the different observation models which can be used in the RoboCup soccer environments. Moreover, we investigate the use of two different particle filtering strategies: the well known Sample Importance Resampling (SIR) filter, and the Auxiliary Variable Particle filter (APF).


Mobile Robot Particle Filter Motion Model Observation Model Mobile Robotic 
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 2007

Authors and Affiliations

  • Luca Marchetti
    • 1
  • Giorgio Grisetti
    • 1
  • Luca Iocchi
    • 1
  1. 1.Dipartimento di Informatica e Sistemistica, Università di Roma “La Sapienza”, Via Salaria 113 00198 RomeItaly

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