A Comparison of Bayesian Prediction Techniques for Mobile Robot Trajectory Tracking

TL;DR

This paper presents an assessment of different estimation and prediction techniques applied to the tracking of multiple robots, and 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.

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.

Figures (14)

• Figure 1: Extended Kalman Filter (EKF)
• Figure 2: Particle Filter (PF)
• Figure 3: Robot coordinate frame $\left \{\mathbf{x^r},\mathbf{x^r}\right \}$ fixed to the robot's body (local coordiantes) and the traction forces $f_1$, $f_2$ and $f_3$ of the wheels.
• Figure 4: World coordinate frame $\left \{\mathbf{x^w},\mathbf{y^w}\right \}$ fixed to the court (global coordinates) and two teams of RoboCup F-180 robots.
• Figure 5: Forces acting on the robot and velocity components (see Table \ref{['tab:symbol']} for notation details).