A Comparative Analysis of Particle Filter Based Localization Methods

The knowledge of the pose and the orientation of a mobile robot in its operating environment is of utmost importance for an autonomous robot. Techniques solving this problem are referred to as self-localization algorithms. 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). The results of the experiments presented in this work show how the localization’s performances are affected by the choices in the Particle Filter implementation, and aims to provide additional guidelines in developing Particle Filter based algorithms.