Computer Science ›› 2011, Vol. 38 ›› Issue (2): 199-201.
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ZHAO Zeng-shun,SHEN Ji-bi,WANG Ji-zhen,HOU Zeng-guang,TAN Min
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Abstract: This article presented a survey of the most common probabilistic models for self localization algorithm of mobile robot. We proposed a general I3ayesian inference framework which is deduced in detail through a combination of Markov assumption with 13aycsian rule. Under such general framework, we gave a review of the main probabilistic models such as Kalman Filtering Series, Multi-hypothesis Localization, Markov Model Localizations and Monte Carlo localization, etc. , all of which can be captured under this single formalism. This will provide readers a global view of this literature. We emphasized the implementation and drawbacks of Monte Carlo Localization, which is considered as one of the most promising method.
Key words: Bayesian filtering, Robot localization, Monte carlo localization, Markov localization
ZHAO Zeng-shun,SHEN Ji-bi,WANG Ji-zhen,HOU Zeng-guang,TAN Min. Research on Self-localization Methods for Mobile Robots Based on Bayes Filter[J].Computer Science, 2011, 38(2): 199-201.
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