Title: Filtering based sensor fusion positioning methods: literature review

Authors: Michael Peiris; Moustafa El-Gindy; Haoxiang Lang

Addresses: Department of Automotive and Mechatronics Engineering, University of Ontario Institute of Technology, 2000 Simcoe St. N, Oshawa, ON L1G 0C5, Ontario, Canada ' Department of Automotive and Mechatronics Engineering, University of Ontario Institute of Technology, 2000 Simcoe St. N, Oshawa, ON L1G 0C5, Ontario, Canada ' Department of Automotive and Mechatronics Engineering, University of Ontario Institute of Technology, 2000 Simcoe St. N, Oshawa, ON L1G 0C5, Ontario, Canada

Abstract: This paper presents a detailed review of filtering-based techniques for localising mobile robots. Localisation and increasing accuracy of positioning is a key field of research for autonomous navigation and mobile robotics. Several techniques based on the Kalman Filter are examined and relevant research and studies using these techniques for localisation are highlighted in the proceeding sections of this paper. The main filtering techniques include: the Linear Kalman Filter, Extended Kalman Filter and Unscented Kalman Filter. The results of presented studies are examined with limitations and meaningful results discussed. In short, this paper aims to summarise recent applications of mobile robot positioning, displaying the current state of the literature and research regarding Kalman Filter-based techniques.

Keywords: multi-wheeled vehicles; autonomous navigation; sensor fusion; Kalman filter; localisation; positioning.

DOI: 10.1504/IJVSMT.2023.135460

International Journal of Vehicle Systems Modelling and Testing, 2023 Vol.17 No.3/4, pp.311 - 325

Received: 15 Sep 2023
Accepted: 18 Sep 2023
Published online: 13 Dec 2023 *

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