An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

Abstract

Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other positionestimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch values are used for step detection. The step lengths are estimated by using the pitching amplitude. The heading of the pedestrian is estimated by the fusion of magnetometer and gyroscope sensor values. Finally, the position is estimated based on the step length and heading information. The proposed pitch-based step detection algorithm achieves 2.5% error as compared with acceleration-based step detection approaches. The heading estimation proposed in this paper achieves a mean heading error of 4.72 • as compared with the azimuth-and magnetometer-based approaches. The experimental results show that the proposed position-estimation algorithm achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation in this paper. INDEX TERMS Indoor positioning system (IPS), pedestrian dead reckoning (PDR), heading estimation, indoor navigation, Android-based smartphone, quaternion, Kalman filter, sensor fusion.

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

“…A comparison of the most commonly used indoor localization techniques is given in Table 1. [22] Acceleration, angular velocity, magnetometer 1-5% of the traveling distance Compact size, low cost, NLoS # Position/orientation drift, magnetic disturbance, accumulated error in calculation * RSSI: Received signal strength indicator; † ToA/TDoA/AoA: Time or arrival/time difference of arrival/angle of arrival; # Non-line-of-sight.…”

A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

“…The PDR positioning in the proposed model uses our previous work [17] for user position estimation. In [17], we proposed a sensor fusion technique for pitch and roll estimation, a pitch-based step detector algorithm for step detection, step length estimation from step detector, a sensor fusion technique for heading estimation and a position estimator. The pitch values from the proposed sensor fusion technique are used for user step detection.…”

“…The position estimation algorithm takes the user step length and heading values and estimates the current user position. For more details of PDR implementation, refer to our previous work in [17].…”

“…The proposed system uses a sensor fusion framework for combining the PDR results with Wi-Fi localization system results. For PDR approach, we used our previous model proposed in [17]. Our previous PDR model reduced the smartphone sensor errors and provides accurate localization results.…”

A Multi-Mode PDR Perception and Positioning System Assisted by Map Matching and Particle Filtering

“…Lin et al [25] utilized a received signal strength indicator to correct the orientation error of PDR, but the restriction is that it only applies to pedestrians walking in a straight line. In [26,27], some fusion techniques were utilized to improve the performance of low-cost sensors' heading estimation, such as the linear Kalman filter, the extended Kalman filter, the unscented Kalman filter, complementary filters, and the particle filter (PF). However, most of the existing approaches are limited and are only able to perform tracking when the smartphone is carried in a defined or constrained way during the entire walking period, which is not always the case in real life.…”