Sensor Fusion for SLAM Based on Information Theory

  • Authors:

  • Xinzheng Zhang;Ahmad B. Rad;Yiu-Kwong Wong;Yan Liu;Xuemei Ren

  • Affiliations:

  • Department of Electrical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong;School of Engineering Science, Simon Fraser University, Surrey, Canada V3T 0A3;Department of Electrical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong;Department of Automatic Control, Beijing Institute of Technology, Beijing, China 100081;Department of Automatic Control, Beijing Institute of Technology, Beijing, China 100081

  • Venue:
  • Journal of Intelligent and Robotic Systems
  • Year:
  • 2010

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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.