A Pedestrian Dead Reckoning Method with Heuristic Motion Direction Estimation for Multiple Gaits
Ouzhao Zheng, Pin Lyu, Jizhou Lai, Cheng Yuan, Zhimin Li, Yao Shan · 2024
Pedestrian dead reckoning (PDR) based on inertial measurement units (IMU) is widely applied due to its robustness and low requirements for wear. However, traditional PDR methods only estimate forward motion, and cannot accurately position in backward and sideward walking gaits. To this end, we propose a multi-gait PDR method based on heuristic motion direction estimation (HMDE). Firstly, we propose a method for efficient data collection and then implement a neural network model to accurately estimate step length across different gaits. Building upon this, we construct a model to estimate relative direction based on IMU data, and put forward a method to estimate forward direction by making multi-rule judgments on relative motion direction and heading angle. Experimental results demonstrate the potential of this method to improve PDR accuracy across mixed gait of walking forward, sidestepping and walking backward, and the positioning accuracy is accurate to within 4% of the total distance.