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A comparison of Bayesian prediction techniques for mobile robot trajectory tracking1

Published online by Cambridge University Press:  01 September 2008

J. L. Peralta-Cabezas,
M. Torres-Torriti and
M. Guarini-Hermann
J. L. Peralta-Cabezas*
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile.
M. Torres-Torriti*
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile.
M. Guarini-Hermann
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile.
*
*Corresponding authors. E-mail: jlperalt@puc.cl; mtorrest@ing.puc.cl
*Corresponding authors. E-mail: jlperalt@puc.cl; mtorrest@ing.puc.cl
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Summary

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This paper presents a performance comparison of different estimation and prediction techniques applied to the problem of tracking multiple robots. The main performance criteria are the magnitude of the estimation or prediction error, the computational effort and the robustness of each method to non-Gaussian noise. Among the different techniques compared are the well-known Kalman filters and their different variants (e.g. extended and unscented), and the more recent techniques relying on Sequential Monte Carlo Sampling methods, such as particle filters and Gaussian Mixture Sigma Point Particle Filter.

Keywords

Mobile robot trackingLatency compensationBayesian predictionKalman filteringMonte Carlo methodsSigma-Point filters

Information

Type
Article
Information
Robotica , Volume 26 , Issue 5 , September 2008, pp. 571 - 585
Copyright
Copyright © Cambridge University Press 2008

Footnotes

1

This work was supported by Conycit of Chile under Fondecyt Grant 11060251.

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