Abstract
Recently, efficient self-localization methods have been developed, among which probabilistic Monte-Carlo localization (MCL) is one of the most popular. However, standard MCL algorithms need at least 100 samples to compute an acceptable position estimation. This paper presents a novel approach to MCL that uses an adaptive number of samples that drops down to a single sample if the pose estimation is sufficiently accurate. Experiments show that the method remains in this efficient single sample tracking mode for more than 90% of the cycles. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Heinemann, P., Haase, J., & Zell, A. (2007). A novel approach to efficient Monte-Carlo localization in RoboCup. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4434 LNAI, pp. 322–329). Springer Verlag. https://doi.org/10.1007/978-3-540-74024-7_29
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