Pedestrian indoor navigation using foot-mounted IMU with multi-sensor data fusion

As a widely used indoor navigation technology, the inertial measurement unit (IMU)-based method has caught considerate research interest. However, owing to the significant and inherent drift of the sensors, it is difficult to get the accurate trajectory for pedestrian movement estimation. In this paper, a foot-mounted IMU system was used to improve the accuracy of pedestrian trajectory, by fusing information from the multiple sensors. With the Kalman filter combined with the zero-velocity update (ZUPT) method, a reasonably accurate pedestrian trajectory was then obtained. Furthermore, some adjustable parameters were introduced to better correct the estimation of position and velocity. Effectiveness of the proposed method was well verified through the indoor experiments and the long track performance was also tested in runway verification.