Lane-level positioning is a critical technology for supporting assisted driving and autonomous driving applica- tions. However, the Global Navigation Satellite System often falls short in providing reliable positioning (GNSS) due to signal attenuation, obstructions, and multipath in urban areas. Fortunately, typical challenging urban environments, such as tunnels and viaducts, create rich magnetic field features due to abundant ferromagnetic structures, offering an opportunity for magnetic field matching methods to achieve high-precision positioning. This paper presents a novel magnetic field matching/GNSS/Inertial Navigation System (INS) fusion algorithm designed for continuous lane-level positioning in complex environments using cost-effective sensors and computation-saving algorithm. Based on the traditional GNSS/INS tight integration algorithm, this research ensures the performance of the positioning system by enhancing the magnetic field matching and fusion positioning algorithms. First, a coarse-fine magnetic profile matching method is proposed to address the accuracy degradation resulting from the travel distance error of INS-derived trajectory. Second, the magnetic field matching position updates are performed in the vehicle frame, which enables more precise position error modeling. The proposed solution is evaluated through five field tests, covering over 200 kilometers of challenging urban roads. The results demonstrate mean CDF95 position errors of 2.09 m, 1.09 m, and 0.87 m in the forward, lateral, and vertical directions, respectively, and 94.67% accuracy on lane-determination.


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    Titel :

    MGINS: A Lane-Level Localization System for Challenging Urban Environments Using Magnetic Field Matching/GNSS/INS Fusion


    Beteiligte:
    Niu, Xiaoji (Autor:in) / Ding, Longyang (Autor:in) / Wang, Yan (Autor:in) / Kuang, Jian (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.10.2024


    Format / Umfang :

    10052515 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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