Research on High Precision Attitude Estimation Based on Gait Cycle Modeling with IMU for PDR

Indoor positioning has a significant demand in various applications, however, the existing indoor positioning technologies may not fully satisfy the practical requirements. Pedestrian Dead Reckoning (PDR) is a commonly employed indoor positioning technology due to its lower requirement for additional infrastructure or exhaustive surveys. And the attitude estimation is a critical step in PDR which affects the accuracy of positioning most. Two challenges impact the accuracy of attitude estimation using inertial measurement unit (IMU), namely the insufficient accuracy of acceleration measurements as gravity and the accumulated error during the integral of low precision gyroscopes. To address these challenges, this paper utilizes the strong correlation between harmful acceleration and gait cycle to propose a gravity estimation algorithm that models the gait cycle by Kalman Filter and removes the harmful acceleration, furthermore, enhances the accuracy of attitude estimation and localization precision. The estimation of the gravity in body frame can be decomposed into the estimation of its mean and its variations. The mean estimation of the gravity is achieved by utilizing the mean acceleration in each step. Meanwhile, the gravity variation is estimated by gait modeling through Kalman Filter based on the gait cycle of pedestrian. The estimated gravity is fused with angular velocity in the Error State Extended Kalman Filter (ES-EKF) to accomplish attitude estimation. Experiments are conducted on seven males and four females in two different locations. The results demonstrate that the proposed algorithm is capable of adapting to different individuals, motion velocities, effectively improving indoor pedestrian localization precision.