NDLI: Neural network based extended Kalman filter for localization of mobile robots

Content Provider	IEEE Xplore Digital Library
Author	Zhuo Wei Yang, S.X.
Copyright Year	2011
Description	Author affiliation: Advanced Robotics & Intelligent System (ARIS) Lab, School of Engineering, University of Guelph, Ontario, Canada (Zhuo Wei; Yang, S.X.)
Abstract	This paper studies the localization of a mobile robot based on neural network based extended Kalman filter (NNEKF) algorithm. Extended Kalman filter (EKF) is used to fuse the information acquired from both the robot optical encoders and ultrasonic sensors in order to estimate the current robot position and orientation. Then the error covariance of the EKF is tracked by the covariance matching technique. When the output of the matching technique does not meet the certain condition, a neural network is employed to modify the system noise covariance matrix. Simulation results demonstrate that, with the comparison to the odometry and the standard EKF method under the same error divergence condition, the proposed algorithm effectively improves the accuracy of the localization of the mobile robot system and prevents the filter divergence.
Starting Page	937
Ending Page	942
File Size	1537695
Page Count	6
File Format	PDF
ISBN	9781612846989
e-ISBN	9781612847009
DOI	10.1109/WCICA.2011.5970654
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-21
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kalman filters Artificial neural networks Mobile robots Robot sensing systems Covariance matrix Neural Networks Mobile Robot Localization extended Kalman filter
Content Type	Text
Resource Type	Article

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