Accurate and continuous positioning in global navigation satellite system (GNSS) deprived urban areas is crucial for autonomous vehicles and mobile mapping systems. To achieve this goal, we propose a stereo visual-inertial odometry approach using a multistate constraint Kalman filter (MSCKF). In contrast with the conventional MSCKF, in which an inertial navigation system (INS) propagates the vehicle motion, and then the propagation is corrected by measurements of salient features extracted from images of a single camera, we update the propagation with observations extracted from images of a stereo pair of cameras. This way, additional constraints across the stereo pairs of images are exploited to improve the pose estimation. Experimental results on several KITTI datasets show that the stereo MSCKF outperforms the mono one achieving an average positioning error of 0.9% of the trajectory length compared to 1.3% for the mono approach and 2.5% for INS-only integration. These results show that visual-inertial odometry has a promising potential for vehicle positioning in short periods of GNSS signal outage.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle Positioning in GNSS-Deprived Urban Areas by Stereo Visual-Inertial Odometry


    Contributors:

    Published in:

    Publication date :

    2018-06-01


    Size :

    2113737 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Stereo Visual Inertial Odometry for Unmanned Aerial Vehicle Autonomous Flight

    Liu, Quanpan / Wang, Zhengjie / Wang, Huan | Springer Verlag | 2019


    Stereo Visual Inertial Odometry for Unmanned Aerial Vehicle Autonomous Flight

    Liu, Quanpan / Wang, Zhengjie / Wang, Huan | British Library Conference Proceedings | 2020


    WHEEL ODOMETRY AIDED VISUAL-INERTIAL ODOMETRY FOR LAND VEHICLE NAVIGATION IN WINTER URBAN ENVIRONMENTS

    Huang, Cheng / Jiang, Yang / O Keefe, Kyle | British Library Conference Proceedings | 2020



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

    Jung, Jae Hyung / Cha, Jaehyuck / Chung, Jae Young et al. | IEEE | 2022