A comparison of Bayesian prediction techniques for mobile robot trajectory tracking

Peralta-Cabezas, JL; Torres-Torriti, M; Guarini-Hermann, M

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. © 2008 Cambridge University Press.

Más información

Título según WOS: A comparison of Bayesian prediction techniques for mobile robot trajectory tracking
Título según SCOPUS: A comparison of Bayesian prediction techniques for mobile robot trajectory tracking
Título de la Revista: ROBOTICA
Volumen: 26
Número: 5
Editorial: CAMBRIDGE UNIV PRESS
Fecha de publicación: 2008
Página de inicio: 571
Página final: 585
Idioma: English
URL: http://www.journals.cambridge.org/abstract_S0263574708004153
DOI:

10.1017/S0263574708004153

Notas: ISI, SCOPUS