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Adaptive Multi-Sensor Fusion Localization Method Based on Filtering

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  • Zhihong Wang

    (Hubei Key Laboratory of Modern Auto Parts Technology, Wuhan University of Technology, Wuhan 470030, China
    Auto Prats Technology Hubei Collaborative Innovation Center, Wuhan University of Technology, Wuhan 470030, China
    Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering, Wuhan University of Technology, Wuhan 470030, China)

  • Yuntian Bai

    (Hubei Key Laboratory of Modern Auto Parts Technology, Wuhan University of Technology, Wuhan 470030, China
    Auto Prats Technology Hubei Collaborative Innovation Center, Wuhan University of Technology, Wuhan 470030, China
    Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering, Wuhan University of Technology, Wuhan 470030, China)

  • Jie Hu

    (Hubei Key Laboratory of Modern Auto Parts Technology, Wuhan University of Technology, Wuhan 470030, China
    Auto Prats Technology Hubei Collaborative Innovation Center, Wuhan University of Technology, Wuhan 470030, China
    Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering, Wuhan University of Technology, Wuhan 470030, China)

  • Yuxuan Tang

    (Hubei Key Laboratory of Modern Auto Parts Technology, Wuhan University of Technology, Wuhan 470030, China
    Auto Prats Technology Hubei Collaborative Innovation Center, Wuhan University of Technology, Wuhan 470030, China
    Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering, Wuhan University of Technology, Wuhan 470030, China)

  • Fei Cheng

    (Commercial Product R&D Institute, Dongfeng Automobile Co., Ltd., Wuhan 470000, China)

Abstract

High-precision positioning is a fundamental requirement for autonomous vehicles. However, the accuracy of single-sensor positioning technology can be compromised in complex scenarios due to inherent limitations. To address this issue, we propose an adaptive multi-sensor fusion localization method based on the error-state Kalman filter. By incorporating a tightly coupled laser inertial odometer that utilizes the Normal Distribution Transform (NDT), we constructed a multi-level fuzzy evaluation model for posture transformation states. This model assesses the reliability of Global Navigation Satellite System (GNSS) data and the laser inertial odometer when GNSS signals are disrupted, prioritizing data with higher reliability for posture updates. Real vehicle tests demonstrate that our proposed positioning method satisfactorily meets the positioning accuracy and robustness requirements for autonomous driving vehicles in complex environments.

Suggested Citation

  • Zhihong Wang & Yuntian Bai & Jie Hu & Yuxuan Tang & Fei Cheng, 2024. "Adaptive Multi-Sensor Fusion Localization Method Based on Filtering," Mathematics, MDPI, vol. 12(14), pages 1-14, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2225-:d:1436546
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    References listed on IDEAS

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    1. Ernest H. Forman & Saul I. Gass, 2001. "The Analytic Hierarchy Process---An Exposition," Operations Research, INFORMS, vol. 49(4), pages 469-486, August.
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