artículo
A comparison of Bayesian prediction techniques for mobile robot trajectory tracking
Fecha
2008Registro en:
10.1017/S0263574708004153
1469-8668
0263-5747
WOS:000259621300002
Autor
Peralta Cabezas, J. L.
Torres Torriti, M.
Guarini Hermann, M.
Institución
Resumen
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.