Opportunistic Radio Map Creation With Foot-Mounted IMUs: A Novel Approach for Indoor Positioning

Accurate indoor positioning remains a significant challenge, primarily due to the lack of global navigation satellite system (GNSS) signals and the inherent complexity of indoor environments. While foot-mounted inertial measurement unit (IMU) systems provide infrastructure-free operation, they suffer from cumulative drift over time unless externally corrected. In this work, we present a hybrid localization framework that fuses foot-mounted IMU data with received signal strength indicator (RSSI) measurements from ambient Wi-Fi access points (APs). Unlike conventional fingerprinting approaches that demand laborious manual labeling, our method autonomously builds a probabilistic radio map during routine pedestrian movement by continuously associating accurate pedestrian dead reckoning (PDR) positions with concurrent Wi-Fi scans. The collected RSSI data are discretized onto a two-dimensional spatial grid and statistically normalized to accommodate signal strength variations across diverse devices. During online operation, a sequential importance resampling (SIR) particle filter (PF) is employed to compare live RSSI measurements against the previously constructed radio map, facilitating robust and accurate position estimation without reliance on inertial sensors or prior knowledge of AP locations. Extensive evaluations in a real-world office environment — using multiple hardware platforms and users — demonstrate that our system consistently achieves meter-level accuracy under realistic conditions and exhibits strong generalization across different devices. These results highlight the potential of our approach as a lightweight and scalable solution for indoor localization.