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Sensor Fusion for SLAM Based on Information Theory

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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.

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Authors and Affiliations

  1. Department of Electrical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Xinzheng Zhang & Yiu-Kwong Wong

  2. School of Engineering Science, Simon Fraser University, 250-13450, 102 Avenue, Surrey, BC, V3T 0A3, Canada

    Ahmad B. Rad

  3. Department of Automatic Control, Beijing Institute of Technology, Beijing, 100081, China

    Yan Liu & Xuemei Ren

Authors
  1. Xinzheng Zhang
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  2. Ahmad B. Rad
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  3. Yiu-Kwong Wong
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  4. Yan Liu
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  5. Xuemei Ren
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Correspondence to Ahmad B. Rad.

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Zhang, X., Rad, A.B., Wong, YK. et al. Sensor Fusion for SLAM Based on Information Theory. J Intell Robot Syst 59, 241–267 (2010). https://doi.org/10.1007/s10846-010-9399-6

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  • DOI: https://doi.org/10.1007/s10846-010-9399-6

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