A Comparison of Bayesian Prediction Techniques for Mobile Robot Trajectory Tracking

Fuente: arXiv
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Hauptverfasser: Peralta-Cabezas, Jose Luis, Torres-Torriti, Miguel, Guarini-Hermann, Marcelo
Format: Preprint
Veröffentlicht: 2026
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author Peralta-Cabezas, Jose Luis
Torres-Torriti, Miguel
Guarini-Hermann, Marcelo
author_facet Peralta-Cabezas, Jose Luis
Torres-Torriti, Miguel
Guarini-Hermann, Marcelo
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2602_15354
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A Comparison of Bayesian Prediction Techniques for Mobile Robot Trajectory Tracking
Peralta-Cabezas, Jose Luis
Torres-Torriti, Miguel
Guarini-Hermann, Marcelo
Robotics
Systems and Control
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.
title A Comparison of Bayesian Prediction Techniques for Mobile Robot Trajectory Tracking
topic Robotics
Systems and Control
url https://arxiv.org/abs/2602.15354