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Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots

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  • Rigatos, Gerasimos G.

Abstract

Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots’ state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations.

Suggested Citation

  • Rigatos, Gerasimos G., 2010. "Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(3), pages 590-607.
    • Handle: RePEc:eee:matcom:v:81:y:2010:i:3:p:590-607
    DOI: 10.1016/j.matcom.2010.05.003
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    References listed on IDEAS

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    1. G. Rigatos & S. Tzafestas, 2007. "Extended Kalman filtering for fuzzy modelling and multi-sensor fusion," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 13(3), pages 251-266, June.
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    Cited by:

    1. My, Chu Anh & Makhanov, Stanislav S. & Van, Nguyen A. & Duc, Vu M., 2020. "Modeling and computation of real-time applied torques and non-holonomic constraint forces/moment, and optimal design of wheels for an autonomous security robot tracking a moving target," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 170(C), pages 300-315.

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