In this paper, we propose VINS-Vehicle, a novel tightly-coupled vehicle dynamics extension to visual-inertial navigation system (VINS) framework. Degenerate motions, such as uniform linear motions or uniform circular motions, which are most common for a ground vehicle, are not observable for a monocular VINS. Therefore, VINS cannot be applied to vehicles, due to difficulties in initialization and low accuracy. To address this limitation, we extend VINS to tightly coupled with pre-integrated high-frequency motion information based on a two degree-of-freedom (DOF) vehicle dynamics model. By loosely aligning the structure from motion (SfM) results, pre-integrated IMU measurements and motion information, the system can be robustly initialized. A tightly-coupled, sliding window optimization method is proposed to obtain an accurate visual-inertial-dynamics odometry result. The experiments show that the system achieved significantly higher positioning accuracy compared with existing VINS methods. Moreover, the proposed method is robust in a texture-less underground parking lot and dynamic outdoor environments.


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    Titel :

    VINS-Vehicle: A Tightly-Coupled Vehicle Dynamics Extension to Visual-Inertial State Estimator


    Beteiligte:
    Kang, Rong (Autor:in) / Xiong, Lu (Autor:in) / Xu, Mingyu (Autor:in) / Zhao, Junqiao (Autor:in) / Zhang, Peizhi (Autor:in)


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    2964919 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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