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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  and
Ching-Chih Tsai
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 author. E-mail: cctsai@dragon.nchu.edu.tw
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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.

Keywords

Extended information filterFuzzy logicsLocalizationPose trackingLaser scannerMobile robotUltrasonics
Type
Article
Information
Robotica , Volume 26 , Issue 2 , March 2008 , pp. 241 - 254
Copyright
Copyright © Cambridge University Press 2007

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