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

The state estimation on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SE_{2}(3)$ </tex-math></inline-formula> 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.