Advanced sensor fusion and localization techniques for autonomous systems: A review and new approaches
1 Independent Researcher, Arizona, USA.
2 OneAdvanced, UK.
3 Nigeria Inter-bank Settlement System Plc (NIBSS), Nigeria.
4 Independent Researcher, London, UK.
5 Information Systems - Business Analytics, Eastern Michigan University, Ypsilanti, Michigan, USA.
Review
International Journal of Frontline Research in Engineering and Technology, 2024, 02(01), 051–060.
Article DOI: 10.56355/ijfret.2024.2.1.0022
Publication history:
Received on 17 July 2024; revised on 26 August 2024; accepted on 29 August 2024
Abstract:
Accurate localization in dynamic environments is a critical challenge for autonomous systems, particularly in GPS-denied settings and under sensor noise or failure conditions. This review paper explores state-of-the-art sensor fusion and localization techniques, including Kalman filters, particle filters, and machine learning-based approaches. The paper identifies key challenges such as operating in GPS-denied environments, managing sensor noise and failure, and ensuring scalability and real-time processing in complex scenarios. To address these issues, the paper proposes enhanced sensor fusion methods, advanced localization algorithms, and hybrid approaches that integrate traditional techniques with machine learning. These innovations are designed to improve autonomous systems' accuracy, reliability, and adaptability in increasingly complex and unpredictable environments. The paper also outlines validation strategies to ensure the effectiveness of these new methodologies, paving the way for future advancements in the field.
Keywords:
Autonomous Systems; Sensor Fusion; Localization; GPS-Denied Environments; Machine Learning
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0