Factor Graph Optimization Enhanced Pedestrian Dead Reckoning With Dual-Foot-Mounted IMUs

Pedestrian dead reckoning (PDR) utilizes foot-mounted inertial sensors as a State-of-the-Art technique for indoor positioning. In this letter, we introduce an integration of a factor graph optimization (FGO) framework with navigation data from dual-foot-mounted inertial measurement units (IMUs), thus enhancing the accuracy of pedestrian localization. The use of FGO allows for the effective utilization of historical sensor data to improve current state estimation accuracy. Recognizing the potential for sensor error drift over time, we have developed a factor node tailored with pedestrian stride constraints to mitigate error propagation. We conducted several experiments with two low-cost IMUs to evaluate the effectiveness of our proposed method. Supported by numerical analysis, the results show that by incorporating historical information, FGO better explores the correlation between the two feet to significantly improve positioning accuracy, although it increases the computational time, which is negligible.