Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31589
Title: Sensor fusion for SLAM based on information theory
Authors: Zhang, X
Rad, AB
Wong, YK
Liu, Y
Ren, X
Keywords: Entropy
Sensor fusion
SLAM
Issue Date: 2010
Publisher: Springer
Source: Journal of intelligent and robotic systems: theory and applications, 2010, v. 59, no. 3-4, p. 241-267 How to cite?
Journal: Journal of Intelligent and Robotic Systems: Theory and Applications 
Abstract: We present a sensor fusion management technique based on information theory in order to reduce the uncertainty of map features and the robot position in SLAM. The method is general, has no extra postulated conditions, and its implementation is straightforward. We calculate an entropy weight matrix which combines the measurements and covariance of each sensor device to enhance reliability and robustness. We also suggest an information theoretic algorithm via computing the error entropy to confirm the relevant features for associative feature determination. We validate the proposed sensor fusion strategy in EKF-SLAM and compare its performance with an implementation without sensor fusion. The simulated and real experimental studies demonstrate that this sensor fusion management can reduce the uncertainty of map features as well as the robot pose.
URI: http://hdl.handle.net/10397/31589
DOI: 10.1007/s10846-010-9399-6
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