A Sensor Fusion Framework for Indoor Navigation With Online Error Correction and Radio Map Update

The integration of various sensors in smartphones has enabled the use of sensor fusion for indoor positioning. Sensor fusion frameworks, which integrate data from multiple sensors in a smartphone, are being recognized for their potential to improve indoor positioning accuracy. However, real-time errors arising from the dynamic indoor environment can accumulate, posing challenges in detecting and correcting these errors to maintain precise indoor navigation services. In this paper, we propose a novel sensor fusion framework that achieves high positioning accuracy by learning real-time errors accumulated during pedestrian navigation. The proposed system identifies sensor measurement errors, accumulates them, adjusts measurement values based on the accumulated error distribution, and employs these refined data for indoor position estimation. Additionally, we introduce a new technique to detect and exclude anomalous errors during the error adjustment process. The analyzed error information is subsequently used to update the radio map through long-term memory learning during the offline stage, ensuring rapid convergence for our proposed system. In experimental scenarios, the proposed framework achieved an average error distance of approximately 1.68 meters, which is a significant improvement after error correction.