Filtering based sensor fusion positioning methods: literature review

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.

• Record URL:
  • https://doi.org/10.1504/IJVSMT.2023.135460
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/17456436
• Supplemental Notes:
  • Copyright © 2024 Inderscience Enterprises Ltd.
• Authors:
  • Peiris, Michael
  • El-Gindy, Moustafa
  • Lang, Haoxiang
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 311-325
• Serial:
  • International Journal of Vehicle Systems Modelling and Testing
  • Volume: 17
  • Issue Number: 3-4
  • Publisher: Inderscience Enterprises Limited
  • ISSN: 1745-6436
  • EISSN: 1745-6444
  • Serial URL: http://www.inderscience.com/jhome.php?jcode=IJVSMT

Subject/Index Terms

• TRT Terms: Automated vehicle control; Data fusion; Global Positioning System; Kalman filtering; Mobile robots; Navigation
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01906020
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 26 2024 10:02AM