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Intelligent Systems in Motion: A Comprehensive Review on Multi-Sensor Fusion and Information Processing From Sensing to Navigation in Path Planning

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  • Yiyi Cai

    (School of Electronic and Information Engineering, South China University of Technology, China)

  • Tuanfa Qin

    (The Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, China)

  • Yang Ou

    (School of Computer and Electronic Information, Guangxi University, China)

  • Rui Wei

    (School of Computer and Electronic Information, Guangxi University, China)

Abstract

Simultaneous localization and mapping (SLAM) serves as a cornerstone in autonomous systems and has seen exponential growth in its roles, particularly in facilitating advanced path planning solutions. One emerging avenue of research that is rapidly evolving is the incorporation of multi-sensor fusion techniques to enhance SLAM-based path planning. The paper initiates with a thorough review of various sensor types and their attributes before covering a broad spectrum of both traditional and contemporary algorithms for multi-sensor fusion within SLAM. Performance evaluation metrics pertinent to SLAM and sensor fusion are explored. A special focus is laid on the interconnected roles and applications of multi-sensor fusion in SLAM-based path planning, discussing its significance in navigation scenarios as well as addressing challenges such as computational burden and real-time implementation. This paper sets the stage for future developments in creating more robust, resilient, and efficient SLAM-based path planning systems enabled by multi-sensor fusion.

Suggested Citation

  • Yiyi Cai & Tuanfa Qin & Yang Ou & Rui Wei, 2023. "Intelligent Systems in Motion: A Comprehensive Review on Multi-Sensor Fusion and Information Processing From Sensing to Navigation in Path Planning," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 19(1), pages 1-35, January.
    • Handle: RePEc:igg:jswis0:v:19:y:2023:i:1:p:1-35
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