%0 Journal Article
%T Research on Self-localization Methods for Mobile Robots Based on Bayes Filter<br>基于Bayes滤波的移动机器人定位方法
%A ZHAO Zeng-shun
%A SHEN Ji-bi
%A WANG Ji-zhen
%A HOU Zeng-guang
%A TAN Min
%A <br>赵增顺
%A 沈继毕
%A 王继贞
%A 侯增广
%A 谭民
%J 计算机科学
%D 2011
%I 
%X 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.
%K Bayesian filtering
%K Robot localization
%K Monte carlo localization
%K Markov localization<br>贝叶斯滤波，机器人定位，蒙特卡罗定位，马尔可夫定位
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=05A652427CB7E62683AC5E539783695D&yid=9377ED8094509821&vid=16D8618C6164A3ED&iid=0B39A22176CE99FB&sid=FCD27DC5E1F2EEE7&eid=BC084ACE66B62CC8&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=17