A Pedestrian Navigation System Based on Low Cost IMU

Li, Yan; Wang, Jianguo Jack

doi:10.1017/s0373463314000344

Cited by 17 publications

(7 citation statements)

References 23 publications

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“…Firstly, we calculate the magnitude of acceleration and turn rate collected by the foot-mounted inertial sensor, and then compare the calculation value with a threshold in each step; the ZV detection will be set to one when the calculation result is less than the threshold. Secondly, the stored ZV detection values are further processed; see details from [17].…”

Section: Zero-velocity Updatementioning

confidence: 99%

Performance Enhancement of Indoor Pedestrian Positioning with Two-Order Bayesian Estimation Based on EKF and PF

2017

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Abstract:To improve the accuracy of indoor pedestrian positioning, an indoor pedestrian positioning system with two-order Bayesian estimation based on Extended Kalman Filter (EKF) and Particle Filter (PF) is proposed in this paper. The presented system combines a foot-mounted inertial sensor, a Wi-Fi propagation model and building structure to make good use of these information resources. There are two updates in this system in order to limit the accumulative errors of inertial sensors. In the first update, the inertial navigation system (INS) is the main system in the calculation of pedestrian positioning, and Zero-velocity update (ZUPT) is introduced as the reference to correct the accumulative errors of INS based on EKF. To further limit the accumulative errors of inertial sensors, the estimated results obtained from the first update, including horizontal position information, are introduced as the observations based on PF in the second update; Pedestrian Dead Reckoning (PDR) is the main system in the calculation of pedestrian positioning, and the weight of particles is determined by the Wi-Fi propagation model, building structure information and output of the first update. The results show that the accuracy of positioning is effectively increased.

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Section: Zero-velocity Updatementioning

confidence: 99%

Performance Enhancement of Indoor Pedestrian Positioning with Two-Order Bayesian Estimation Based on EKF and PF

2017

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“…The inertial navigation system (INS) approach, which uses an extended Kalman filter (EKF) and zero velocity updates (ZUPT) for estimating navigation and sensor error states, has also been applied for indoor pedestrian navigation applications [ 13 , 14 , 15 , 16 , 17 ], and many other applications recently [ 18 , 19 , 20 , 21 ]. This approach, which is generally called the INS-EKF-ZUPT approach, uses an inertial measurement unit (IMU) installed on a foot or shoe, and the characteristics of the walking gait cycle, which is composed of stance and swing phases.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Pedestrian Navigation Based on Course Angle Error Estimation Using Cascaded Kalman Filters

Song

Park

2018

Sensors

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An enhanced pedestrian dead reckoning (PDR) based navigation algorithm, which uses two cascaded Kalman filters (TCKF) for the estimation of course angle and navigation errors, is proposed. The proposed algorithm uses a foot-mounted inertial measurement unit (IMU), waist-mounted magnetic sensors, and a zero velocity update (ZUPT) based inertial navigation technique with TCKF. The first stage filter estimates the course angle error of a human, which is closely related to the heading error of the IMU. In order to obtain the course measurements, the filter uses magnetic sensors and a position-trace based course angle. For preventing magnetic disturbance from contaminating the estimation, the magnetic sensors are attached to the waistband. Because the course angle error is mainly due to the heading error of the IMU, and the characteristic error of the heading angle is highly dependent on that of the course angle, the estimated course angle error is used as a measurement for estimating the heading error in the second stage filter. At the second stage, an inertial navigation system-extended Kalman filter-ZUPT (INS-EKF-ZUPT) method is adopted. As the heading error is estimated directly by using course-angle error measurements, the estimation accuracy for the heading and yaw gyro bias can be enhanced, compared with the ZUPT-only case, which eventually enhances the position accuracy more efficiently. The performance enhancements are verified via experiments, and the way-point position error for the proposed method is compared with those for the ZUPT-only case and with other cases that use ZUPT and various types of magnetic heading measurements. The results show that the position errors are reduced by a maximum of 90% compared with the conventional ZUPT based PDR algorithms.

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“…Handling MPs in PDR can only be found in few 3D indoor navigation systems using inertial sensors in the literature. In [25,34], the MP is detected and the ZUPTs are suspended. In the so called activity map matching (AMM), the PDR system is re-calibrated by matching activities to prior known locations.…”

Section: Introductionmentioning

confidence: 99%

Integrating Moving Platforms in a SLAM Agorithm for Pedestrian Navigation

Kaiser

Lang

2018

Sensors

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In 3D pedestrian indoor navigation applications, position estimation based on inertial measurement units (IMUss) fails when moving platforms (MPs), such as escalators and elevators, are not properly implemented. In this work, we integrate the MPs in an upper 3D-simultaneous localization and mapping (SLAM) algorithm which is cascaded to the pedestrian dead-reckoning (PDR) technique. The step and heading measurements resulting from the PDR are fed to the SLAM that additionally estimates a map of the environment during the walk in order to reduce the remaining drift. For integrating MPs, we present a new proposal function for the particle filter implementation of the SLAM to account for the presence of MPs. In addition, a new weighting function for features such as escalators and elevators is developed and the features are learned and stored in the learned map. With this, locations of MPs are favored when revisiting the MPs again. The results show that the mean height error is about 0.1 m and the mean position error is less than 1 m for walks with long distances along the floors, even when using multiple floor level changes with different numbers of floors in a multistory environment. For walks with short walking distances and many floor level changes, the mean height error can be higher (about 0.5 m). The final floor number is in all cases except one correctly estimated.

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A Pedestrian Navigation System Based on Low Cost IMU

Cited by 17 publications

References 23 publications

Performance Enhancement of Indoor Pedestrian Positioning with Two-Order Bayesian Estimation Based on EKF and PF

Performance Enhancement of Indoor Pedestrian Positioning with Two-Order Bayesian Estimation Based on EKF and PF

Enhanced Pedestrian Navigation Based on Course Angle Error Estimation Using Cascaded Kalman Filters

Integrating Moving Platforms in a SLAM Agorithm for Pedestrian Navigation

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