High-Precision Pedestrian Indoor Positioning Method Based on Inertial and Magnetic Field Information

Highlights What are the main findings? Reducing yaw angle errors using magnetic field information and an improved Kalman filter method. A trajectory refinement method using magnetic landmarks and pose graph optimization. What is the implication of the main finding? The proposed method makes full use of indoor magnetic field information. By fusing magnetic heading angle constraints and magnetic landmark matching, the indoor pedestrian localization accuracy is significantly improved.Highlights What are the main findings? Reducing yaw angle errors using magnetic field information and an improved Kalman filter method. A trajectory refinement method using magnetic landmarks and pose graph optimization. What is the implication of the main finding? The proposed method makes full use of indoor magnetic field information. By fusing magnetic heading angle constraints and magnetic landmark matching, the indoor pedestrian localization accuracy is significantly improved.Abstract Long-term and high-precision positioning is the key to the pedestrian indoor positioning method. The estimation methods relying only on the inertial measurement unit (IMU) itself lack external observations that can provide absolute information, and the cumulative error easily leads to the distortion of the calculated trajectory. In this paper, based on the Extended Kalman Filter (EKF) algorithm, the environmental magnetic field information is taken as the external observation quantity, and a positioning method combining inertial navigation and the magnetic field is proposed. The cumulative error is suppressed from both the yaw angle and pedestrian pose, and the overall navigation and positioning accuracy is improved. The experimental results show that the proposed fusion method greatly improves the suppression of yaw angle and displacement errors. In a total distance of 297.08 m, the yaw angle error is reduced from 11.043 degrees to 4.778 degrees, and the position error is reduced from 8.999 m to 0.364 m. The relative average error decreases from 3.02% to 0.12%.