In this article, we propose a new methodology to fuse visual-inertial measurements for land vehicles in a challenging urban environment in which a GNSS signal is not available nor reliable. Motivated by a degenerate case caused by a large bias of a MEMS IMU, we redesign a system model of visual-inertial odometry in a framework of extended Kalman filter. In particular, the system model is propagated through a reduced inertial sensor system composed of a 3-axis gyroscope, a 2-axis accelerometer, and a single-axis odometer. An analytical observability derivation reveals unobservable bases of our estimator, and these directions are resolved by using intermittent position measurements from a GNSS receiver. Furthermore, we inspect the uncertainties of the state vector in a Monte-Carlo simulation that agrees with our theoretical results. The proposed method is validated through the KITTI benchmark dataset and an extensive field testing showing a position drift as 1.25% in tunnels on average and a mean position error of 2.81m in the street canyon over a 6.7km driving.


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    Titel :

    Monocular Visual-Inertial-Wheel Odometry Using Low-Grade IMU in Urban Areas


    Beteiligte:
    Jung, Jae Hyung (Autor:in) / Cha, Jaehyuck (Autor:in) / Chung, Jae Young (Autor:in) / Kim, Tae Ihn (Autor:in) / Seo, Myung Hwan (Autor:in) / Park, Sang Yeon (Autor:in) / Yeo, Jong Yun (Autor:in) / Park, Chan Gook (Autor:in)


    Erscheinungsdatum :

    01.02.2022


    Format / Umfang :

    3777671 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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