With the development of vehicle sensors, unmanned driving has become a research hotspot. Positioning is also considered to be one of the most challenging directions in this field. Aiming at the poor positioning accuracy of vehicles under GNSS denied environments, a lane-level positioning method based on inertial system and vector map information fusion is proposed. A dead reckoning model based on optical fiber IMU and odometer is established, and its positioning error is regarded as a priori information. Furthermore, a map matching model based on HMM is built up. Three validation experiments are carried out and experimental results show that the positioning error can be reduced to less than 30 cm when driving for about 7 min, which proves the effectiveness of the proposed method. Our work may provide a reference for the further improvement of positioning for unmanned driving under GNSS denied environments.


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    Titel :

    Lane Level Positioning Method for Unmanned Driving Based on Inertial System and Vector Map Information Fusion Applicable to GNSS Denied Environments


    Beteiligte:
    Minpeng Dai (Autor:in) / Haoyang Li (Autor:in) / Jian Liang (Autor:in) / Chunxi Zhang (Autor:in) / Xiong Pan (Autor:in) / Yizhuo Tian (Autor:in) / Jinguo Cao (Autor:in) / Yuxuan Wang (Autor:in)


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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