Advanced sensor fusion and localization techniques for autonomous systems: A review and new approaches
Morayo Ogunsina, Christianah Pelumi Efunniyi, Olajide Soji Osundare, Samuel Olaoluwa Folorunsho, Lucy Anthony Akwawa
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.