A pedestrian dead-reckoning system that considers the heel-strike and toe-off phases when using a foot-mounted IMU

Ju, Hojin; Lee, Min Su; Park, So Young; Song, Ju Yeon; Park, Chan Gook

doi:10.1088/0957-0233/27/1/015702

Cited by 36 publications

(40 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ZUPT technique is based on the characteristics of the walking gait of humans. The walking cycle of a human can be divided into two parts, the stance and swing phases, as shown in Figure 1 [ 16 , 46 ]. During the stance phase, the sole of a foot is in contact with the ground partially or entirely.…”

Section: Zero Velocity Update (Zupt) Based Pedestrian Dead Reckonimentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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

Song

Park

2018

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Zero Velocity Update (Zupt) Based Pedestrian Dead Reckonimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Song

Park

2018

Sensors

View full text Add to dashboard Cite

show abstract

“…The method with the infrastructure to estimate the pedestrian position uses Wi-Fi, BLE or IMUs instead of using an infrastructure. The pedestrian dead reckoning (PDR) system based on the IMU has an advantage in that it can estimate the pedestrian position without needing prior information [1][2][3][4][5][6][7][8][9]. Since MEMS-IMU is compact, it has been used in PDR systems.…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

Error Analysis of PDR System Using Dual Foot-mounted IMU

Lee

Park

2019

E3S Web Conf.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…In reality, because of the unpredictable movements of pedestrian, it is usually difficult to judge the state of static or quasi-static. Although there are scholars research the gait characteristics of pedestrian [14][15], motion model [16] and so on. While the motion models, such as walking, running, climbing, jumping and so on, are difficult to be expanded and transplanted, which makes it difficult to ensure the long-term accuracy of indoor positioning when pedestrians change their motion frequently.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical calibration architecture based on Inertial/magnetic sensors for indoor positioning

Liu¹,

Shi²,

Chen³

et al. 2018

EasyChair Preprints

View full text Add to dashboard Cite

Indoor positioning system based on MEMS (Micro-electromechanical Systems) technology is really self-measurement technology which does not depend on any external signal and other infrastructure. Inertial/magnetic sensors based on MEMS technology are typically composed of three orthogonal gyroscopes, three orthogonal accelerometers and three orthogonal magnetometers, which can form AHRS (attitude heading reference system) and pedestrian dead reckoning system. However, the inertial/magnetic sensors can offer good short term positioning in indoor environments, but the absolute position fixes of medium- to long- term have always been the research hotspot and difficulty. Therefore, a hierarchical calibration architecture based on inertial/magnetic sensors is proposed creatively, including the data layer—a axial-based temperature variation model is proposed to calibrate the raw data from MEMS sensors by the analysis and modeling of sensor error characteristics; the signal layer—a triple zero velocity update method is proposed to update integral initial value according to the pedestrians’ gait characteristic; the information layer—an intelligent information fusion method is proposed based on machine learning to calibrate the orientation and positioning. Besides, a 3-D indoor positioning platform is set up based on MPU9250, and the effectiveness of the proposed hierarchical calibration architecture is verified based on the platform.

show abstract

A pedestrian dead-reckoning system that considers the heel-strike and toe-off phases when using a foot-mounted IMU

Cited by 36 publications

References 26 publications

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

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

Error Analysis of PDR System Using Dual Foot-mounted IMU

Hierarchical calibration architecture based on Inertial/magnetic sensors for indoor positioning

Contact Info

Product

Resources

About