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《Modern Electronics Technique》 2010-22
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Application of Iterated Kalman Filtering in Robot Localization

LONG Hui1,HU Li2,ZHOU Yan-yu3(1.Hunan Biological and Electromechanical Polytechnic,Changsha 410126,China;2.Hunan Intelligent Control System Company,Changsha 410001,China;3.College of Information Engineering,Central South University,Changsha 410075,China)  
Localization is one of the most fundamental problem in mobile robots.The localization problem was solved by iterated extended Kalman filtering(IEKF) by combining the reckon reference and global observation information.In the measurement update phase of Kalman filtering,the state estimation is iterated many times until estimation error is lower than the threshold and localization error aroused from Taylor expand is reduced effectively.The convergent stability was improved by the method.Finally,IEKF and EKF methods are compared.the simulation result shows that IEKF is an effective method in localization of mobile robots.
【Fund】: 湖南省教育厅基金资助项目(09C1215)
【CateGory Index】: TP242
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