A case study on sensors and techniques for pedestrian inertial navigation

Patarot, Alexandre; Boukallel, Mehdi; Lamy-Perbal, Sylvie

doi:10.1109/isiss.2014.6782527

Cited by 18 publications

(7 citation statements)

References 5 publications

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“…Compared to other navigation systems, inertial navigation systems (INSs) don't require the installation of additional external equipment [8][9]. The inertial navigation systems are not limited by external environmental conditions, making it a fully autonomous navigation system.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Inertial Navigation with Building Floor Plans for Indoor Environments via Particle Filter

Zhang¹,

Zhang²,

Guo³

et al. 2022

Journal of Compurters

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<p>With the rapid development of smart city, the indoor positioning services became more and more important. During the existing solutions, inertial measurement unit (IMU) with pedestrian dead reckoning (PDR) was a promising scheme since they did not require external equipment in the environment. However, the orientation drift of low-cost IMU limited their application in practical. To address this problem, a zero-velocity update (ZUPT) framework included Kalman filter and particle filter is designed based on the foot-based low-cost IMU and digital floor plan to provide the service of personal navigation. In the designed Smoothing for ZUPT-aided INSs framework, the Kalman filter is used to estimate the position and attitude by zero velocity correction technique. Then, the particle filter is used to improve the localization and heading accuracy by map matching. The position estimation presented in this study achieves an average position error of 1.16 m. The experimental results show that the designed framework can solve the personal navigation problem in the case of building plan information assistance and help improve the accuracy and reliability of continuous position determination of personal navigation systems effectively. </p> <p> </p>

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Section: Introductionmentioning

confidence: 99%

Pedestrian Inertial Navigation with Building Floor Plans for Indoor Environments via Particle Filter

Zhang¹,

Zhang²,

Guo³

et al. 2022

Journal of Compurters

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“…Inertial-based positioning systems can track the position of the user with no pre-installed infrastructures or a geographical fingerprint. These systems are generally in the form of wearable devices, which can be worn on the helmet, belt or shoe of a person [ 9 , 10 ]. Among such systems, the foot-mounted inertial measurement unit (IMU) is preferred in our system [ 11 , 12 ], because the foot-mounted IMU has stance phases, which can be used to suppress the long-term drifts of inertial sensors with the Zero-velocity updates (ZUPT) aided Extended Kalman Filter (EKF) method [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Use of Magnetic Field for Mitigating Gyroscope Errors for Indoor Pedestrian Positioning

Song

et al. 2018

Sensors

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In the field of indoor pedestrian positioning, the improved Quasi-Static magnetic Field (iQSF) method has been proposed to estimate gyroscope biases in magnetically perturbed environments. However, this method is only effective when a person walks along straight-line paths. For other curved or more complex path patterns, the iQSF method would fail to detect the quasi-static magnetic field. To address this issue, a novel approach is developed for quasi-static magnetic field detection in foot-mounted Inertial Navigation System. The proposed method detects the quasi-static magnetic field using the rate of change in differences between the magnetically derived heading and the heading derived from gyroscope. In addition, to eliminate the distortions caused by system platforms and shoes, a magnetometer calibration method is developed and the calibration is transformed from three-dimensional to two-dimensional coordinate according to the motion model of a pedestrian. The experimental results demonstrate that the proposed method can provide superior performance in suppressing the heading errors with the comparison to iQSF method.

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“…In a study by [1], there are two common approaches in placing the INS on a person; foot-mounted and waist-mounted. In the early development in using INS to obtain the position information, Foxlin [2] has proposed to mount the inertial sensors on the shoe to track a pedestrian.…”

Section: Introductionmentioning

confidence: 99%

“…Others have proposed to strap the inertial sensors around the waist [1], [10], [11]. The placement is said to be more userfriendly and practical compared to foot-mounted.…”

Section: Introductionmentioning

confidence: 99%

Real-time PDR based on resource-constrained embedded platform

Muhammad

Salčić

Wang

2015

2015 9th International Conference on Sensing Technology (ICST)

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Standalone inertial navigation system (INS) in indoor pedestrian positioning is becoming imminent as the researchers exploit its small form factor and low power requirement. This will result in small-size, low-power wearable devices that are not obtrusive to the users and yet provide sufficiently accurate pedestrian localization and tracking within. At this stage, most of the recent INS-based indoor pedestrian positioning systems still have to interface with other computing machines such as a laptop or smartphone to perform computationally demanding algorithms. Most of the existing techniques operate in off-line and not real-time mode. In this paper, we propose a real-time indoor pedestrian dead-reckoning system based on embedded INS. The results show that our system successfully track the distance travelled by pedestrians up to an error of three percent with a position update interval less than a second.

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A case study on sensors and techniques for pedestrian inertial navigation

Cited by 18 publications

References 5 publications

Pedestrian Inertial Navigation with Building Floor Plans for Indoor Environments via Particle Filter

Pedestrian Inertial Navigation with Building Floor Plans for Indoor Environments via Particle Filter

Use of Magnetic Field for Mitigating Gyroscope Errors for Indoor Pedestrian Positioning

Real-time PDR based on resource-constrained embedded platform

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