A wearable dual-IMU-based spatio-temporal fusion method for pedestrian inertial navigation systems

To improve the accuracy and robustness of large-scale pedestrian autonomous navigation systems, this paper introduces a spatio-temporal information fusion method utilizing dual IMUs attached on the foot and waist. The proposed approach leverages the distinct periodic characteristics of waist motion to enhance the detection accuracy of zero-velocity observation periods for foot motion, thereby minimizing position drift. By capitalizing on the temporal and spatial consistency between different body segments during walking, the waist motion half-cycle is used as a substitute for the foot walking half-cycle, effectively reducing errors in step cycle detection. Additionally, the method adaptively determines the start and end points of the zero-velocity period by mapping the relationship among zero-velocity duration, walking speed and step cycle. This adaptive adjustment further refines the accuracy of zero-velocity corrections in inertial positioning. Experimental results demonstrate that the proposed method achieves a positioning error percentage of 0.46%, outperforming the traditional fixed-threshold method and adaptive-threshold method with an order of magnitude reduction in positioning error. Meanwhile, it also exhibits superior positioning performance in complex scenarios involving door opening and closing.