People tracking with a mobile robot: a comparison of Kalman and particle filters

Authors:
Nicola Bellotto;Huosheng Hu
Affiliations:
University of Essex, Wivenhoe Park, Colchester, UK;University of Essex, Wivenhoe Park, Colchester, UK
Venue:
RA '07 Proceedings of the 13th IASTED International Conference on Robotics and Applications
Year:
2007

Citing 3
Cited 1

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Abstract

People tracking is an essential part for modern service robots. In this paper we compare three different Bayesian estimators to perform such task: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) Particle Filter. We give a brief explanation of each technique and describe the system implemented to perform people tracking with a mobile robot using sensor fusion. Finally, we report several experiments where the three filters are compared in terms of accuracy and robustness. In particular we show that, for this kind of applications, the UKF can perform as well as a particle filter but at a much lower computational cost.