Abstract
In this paper, we analyze the position errors of the pedestrian dead reckoning (PDR) system using foot-mounted IMU attached to each foot, and implement PDR system using dual foot-mounted IMU to reduce the analyzed error. The PDR system using foot-mounted IMU is generally based on an inertial navigation system (INS). To reduce bias and white noise errors, INS is combined with zero velocity update (ZUPT), which assumes that the pedestrian shoe velocity is zero at the stance phase. Although ZUPT could compensate the velocity and position, the heading drift still occurs. When analyzing the characteristics of the position error, the error shows a symmetrical characteristic. In order to reduce this error, the previous researches compensate for both positions by applying feet position constraints. The algorithm consists of applying a conventional PDR system to each foot and fusion algorithm combining both. The PDR system using foot-mounted IMU, one on each foot, is based on integration approach separately. The positions of both feet should be in a circle with a radius as step length during walking. The designed filter is constrained so that the position of both feet are in a circular boundary. The heading error that is symmetrically drifted is corrected by the position constraint when the pedestrian moves straight. Experimental results show the performance and usability of each previous algorithm to compensate for symmetric heading errors.
Highlights
As interest in indoor location service increases, various studies have been conducted on the technique of estimating the position of a pedestrian in real time
The pedestrian navigation system using the inertial measurement (IMU) -mounted on the shoes is being actively studied because it provides the high accuracy position even with the inertial sensor without any device is installed in advance
The other pedestrian dead reckoning (PDR) system is integration approach, which consists of inertial navigation system (INS), which is applied when the sensor is attached to the shoe [7,8,9]
Summary
As interest in indoor location service increases, various studies have been conducted on the technique of estimating the position of a pedestrian in real time. One of the PDR system using the IMU is parametric approach, which estimates the pedestrian position by accumulating the step length and direction of movement This method is mainly used when the sensor is attached to the waist or held by hand [1,2,3,4,5,6]. The other PDR system is integration approach, which consists of INS, which is applied when the sensor is attached to the shoe [7,8,9]. Foxlin proposed PDR system using the extended Kalman filter (EKF) to estimate and subtract errors with ZUPT during the stance phase This algorithm uses the assumption that the velocity is zero when the shoe stuck to the ground. Experiments show that the characteristics of the analyzed position error are compensated by the prvious algorithm
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