Abstract
The classic fusion localization techniques based on the Kalman filter (KF) framework have been a focus of research community in the past decades, due to the limited computing power of the mobile devices. However, with computing-efficient and sensor-rich smartphones now being a commonplace, it is convenient and meaningful to provide more accurate positioning services for smartphones in an indoor environment. In this paper, we design and develop a tightly coupled fusion platform of Wi-Fi RTT, RSS, and data-driven PDR based on factor graph optimization for locating the consumer-grade smartphones in indoor environment. As compared to the existing PDR solutions, including step model-based approaches and data-driven approaches, the proposed PDR solution with magnetic information constraint can track the relative position change of pedestrians at 20 Hz, while supporting multiple smartphone usage poses. A comprehensive comparison between factor graph optimization (FGO), KF frame and its variants is also performed. The experimental results demonstrate that the proposed fusion platform achieves an average positioning accuracy of 0.39 m. In addition, it also improves the accuracy of EFK and ARKF by 45.83% and 27.78%, respectively. The analysis shows that as the smartphone computing performance continues to improve, the FGO-based sensor fusion gradually replaces the KF frame and its variants.
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