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

Gerasimos G. Rigatos

Mathematics and Computers in Simulation (MATCOM), 2010, vol. 81, issue 3, 590-607

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.

Keywords: Extended Kalman Filtering; Particle Filtering; State estimation; Sensor fusion; Motion control (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:81:y:2010:i:3:p:590-607

DOI: 10.1016/j.matcom.2010.05.003

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