The state estimation on $SE_{2}(3)$ Lie Group has been proven to have the ability to improve the consistency of the estimated results. They are called invariant state estimation approaches, including filter-based ones and optimization-based ones. Precise Point Positioning (PPP) is a Global Navigation Satellite System (GNSS) positioning technology which can achieve high-precision positioning without commercial base stations. Visual-Inertial Odometry (VIO) combines Visual-SLAM and IMU, realizing a more robust local pose estimation than either of the two. In this paper, the invariant optimization approach has been applied to fuse PPP/INS/Visual-SLAM. The proposed positioning system in our paper is called In-P3VINS. All raw data of the In-P3VINS is modeled and optimized under an invariant factor graph framework. In particular, the carrier phase measurement is utilized by adding the phase ambiguity into the estimated states. Finally, In-P3VINS is evaluated in both simulation experiments and real-world experiments. In the simulation experiments, the accuracy and consistency of In-P3VINS are superior to the other compared methods. In the real-world experiments, In-P3VINS has the most accurate results.


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    Title :

    In-P3VINS: Tightly-Coupled PPP/INS/Visual SLAM Based on Invariant Optimization Approach


    Contributors:
    Li, Tao (author) / Hua, Tong (author) / Fu, Minglei (author) / Zhang, Wen-An (author) / Pei, Ling (author) / Yu, Wenxian (author) / Truong, Trieu-Kien (author)


    Publication date :

    2025-07-01


    Size :

    5140210 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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