In this paper, a three-dimensional vision-aided method is proposed to improve global navigation satellite system (GNSS) real-time kinematic (RTK) positioning. To mitigate the impact of reflected non-line-of-sight (NLOS) reception, a sky-pointing camera with a deep neural network was employed to exclude these measurements. However, NLOS exclusion results in distorted satellite geometry. To fill this gap, complementarity between the low-lying visual landmarks and the healthy but high-elevation satellite measurements was explored to improve the geometric constraints. Specifically, inertial measurement units, visual landmarks captured by a forward-looking camera, and healthy GNSS measurements were tightly integrated via sliding window optimization to estimate the GNSS-RTK float solution. The integer ambiguities and the fixed GNSS-RTK solution were then resolved. The effectiveness of the proposed method was verified using several challenging data sets collected in urban canyons in Hong Kong.


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    Title :

    3D Vision Aided GNSS Real-Time Kinematic Positioning for Autonomous Systems in Urban Canyons


    Contributors:
    Weisong Wen (author) / Xiwei Bai (author) / Li-Ta Hsu (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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