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Improved global localization of an indoor mobile robot via fuzzy extended information filtering

Published online by Cambridge University Press:  01 March 2008

Hung-Hsing Lin
Affiliation:
Department of Electrical Engineering, National Chung Hsing University, 250, Kuo-Kuang Road, Taichung 40227, Taiwan, R.O.C
Ching-Chih Tsai
Affiliation:
Department of Electrical Engineering, National Chung Hsing University, 250, Kuo-Kuang Road, Taichung 40227, Taiwan, R.O.C
Corresponding
E-mail address:

Summary

Global localization of mobile robots has been well studied using the extended Kalman filter (EKF) method. This paper presents a fuzzy extended information filtering (FEIF) approach to improving global localization of an indoor autonomous mobile robot with ultrasonic and laser scanning measurements. A real-time FEIF algorithm is proposed to improve accuracy of static global pose estimation via multiple ultrasonic data. By fusing odometric, ultrasonic, and laser scanning data, a real-time FEIF-based pose tracking algorithm is developed to improve accuracy of the robot's continuous poses. Several experimental results are performed to confirm the efficacy of the proposed methods.

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Article
Copyright
Copyright © Cambridge University Press 2007

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