A Comparison of Bayesian Prediction Techniques for Mobile Robot Trajectory Tracking

TL;DR

This paper compares various Bayesian prediction methods, including Kalman filters and particle filters, for mobile robot trajectory tracking, focusing on accuracy, computational efficiency, and robustness to noise.

Contribution

It provides a comprehensive performance evaluation of traditional and recent Bayesian prediction techniques for multi-robot tracking.

Findings

Kalman filters perform well with Gaussian noise.

Particle filters show robustness to non-Gaussian noise.

Trade-offs exist between computational effort and accuracy.

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.