@article{ref1,
title="Filtering based sensor fusion positioning methods: literature review",
journal="International journal of vehicle modelling and testing",
year="2023",
author="Peiris, Michael and Gindy, Moustafa El and Lang, Haoxiang",
volume="17",
number="3/4",
pages="311-325",
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.<p /> <p>Language: en</p>",
language="en",
issn="1745-6436",
doi="10.1504/IJVSMT.2023.135460",
url="http://dx.doi.org/10.1504/IJVSMT.2023.135460"
}