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).
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© 2007 Springer-Verlag Berlin Heidelberg
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Marchetti, L., Grisetti, G., Iocchi, L. (2007). A Comparative Analysis of Particle Filter Based Localization Methods. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds) RoboCup 2006: Robot Soccer World Cup X. RoboCup 2006. Lecture Notes in Computer Science(), vol 4434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74024-7_44
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DOI: https://doi.org/10.1007/978-3-540-74024-7_44
Publisher Name: Springer, Berlin, Heidelberg
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