Research on Self-localization Methods for Mobile Robots Based on Bayes Filter
基于Bayes滤波的移动机器人定位方法

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.