High-Precision Indoor Positioning Method Based on Multi-Feature Fusion of Inertial Sensor Network

Highly accurate positioning information can bring higher enhancement and value to basic applications. In the absence of infrastructure or third-party equipment, relying only on the inertial measurement unit (IMU) itself results in severe positioning drift. Aiming this challenge, we propose a high-precision localization method based on multi-feature fusion of body-area inertial sensor networks to achieve reliable and low-drift localization over long periods of time without the need for third-party assistance. The proposed method mainly includes three parts: the information of foot and shank are fused based on a ball hinge model to realize accurate and adaptive step length estimation under variable speed motion; the yaw angle is obtained by weighted fusion of inertial nodes mounted at the waist, shank, and foot; based on global multi-feature fusion with landmarks, the removal of stage historical errors of the position and location refinement are realized. The experimental results show that the proposed method can better suppress the dead reckoning error and effectively improve the positioning accuracy compared with the traditional single-IMU based dead reckoning method. Compared to another multi-IMU fusion approach, our method shows outperformance in the long-distance (1270 meters, 13 minutes). The average yaw angle error and average positioning error of the proposed method are 3.5 degrees and 3.3 meters, respectively, and it can better adapt to the variable speed movement.