An Adaptive Weighted GNSS/VINS/Wi-Fi RTT-based Seamless Positioning System for Smartphone

Existing positioning methods have limitations in accuracy and reliability for seamless positioning. Global Navigation Satellite System (GNSS) faces multipath and signal blockage issues, especially indoors. Wi-Fi positioning solutions are mostly restricted to indoors due to large infrastructure requirements. Infrastructure-independent positioning systems, such as visual-inertial navigation systems (VINS), are limited to providing relative pose estimation and are affected by environmental luminance. This paper presents an approach for smartphone seamless positioning by adaptively fusing multi-sensor data from GNSS, Wi-Fi Round Trip Time (RTT) and VINS using factor graph optimization (FGO). The adaptive weighted FGO algorithm optimizes the estimation of the state variables by minimizing the loss function of selected factors with scaled covariance. Combining the strengths of GNSS, Wi-Fi RTT, and VINS, the proposed system achieves improved positioning accuracy and robustness, and experimental results demonstrate our method’s effectiveness in various scenarios.