A Comparison of Bayesian Prediction Techniques for Mobile Robot Trajectory Tracking

Abstract

This paper presents an assessment of different estimation and prediction techniques applied to the tracking of multiple robots. The main assessment criteria are the magnitude of the estimation or prediction error, the computational effort and the robustness of each method under non-Gaussian noise. Among the different techniques compared are the well known Kalman filters and their different variants (extended and unscented), and the more recent techniques relying on sequential Monte Carlo sampling methods, such as particle filters, and sigma-points filters