A comparison of bayesian prediction techniques for mobile robot trajectory tracking1

Authors:
J. l. Peralta-cabezas;M. Torres-torriti;M. Guarini-hermann
Affiliations:
Department of electrical engineering, pontificia universidad católica de chile, vicuña mackenna 4860, casilla 306-22, santiago, chile.;Department of electrical engineering, pontificia universidad católica de chile, vicuña mackenna 4860, casilla 306-22, santiago, chile.;Department of electrical engineering, pontificia universidad católica de chile, vicuña mackenna 4860, casilla 306-22, santiago, chile.
Venue:
Robotica
Year:
2008

Citing 11
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Abstract

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.