Work

A comparison of Bayesian prediction techniques for mobile robot trajectory tracking

Year: 2008

Type: article

Abstract: SUMMARY 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.

Source: Robotica

DOI: 10.1017/s0263574708004153

Authors: J. L. Peralta-Cabezas, M. Torres-Torriti, M. Guarini-Hermann

Institution: Pontificia Universidad Católica de Chile

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Else Kröner-Fresenius-Stiftung, National Commission for Science and Technology

OpenAlex ID: W2167168312

FWCI: 0.528

Cites: 36

Cited by: 9

Related to: 10