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).

A comparative analysis of particle filter based localization methods / Marchetti, Luca; Grisetti, Giorgio; Iocchi, Luca. - 4434:(2007), pp. 442-449. (Intervento presentato al convegno 10th International RoboCup Symposium tenutosi a Bremen, GERMANY) [10.1007/978-3-540-74024-7_44].

A comparative analysis of particle filter based localization methods

MARCHETTI, Luca;GRISETTI, GIORGIO;IOCCHI, Luca
2007

Abstract

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).
2007
10th International RoboCup Symposium
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A comparative analysis of particle filter based localization methods / Marchetti, Luca; Grisetti, Giorgio; Iocchi, Luca. - 4434:(2007), pp. 442-449. (Intervento presentato al convegno 10th International RoboCup Symposium tenutosi a Bremen, GERMANY) [10.1007/978-3-540-74024-7_44].
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