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Proceedings ArticleDOI

A case study on sensors and techniques for pedestrian inertial navigation

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TLDR
In this article, a comparison of the performance in realistic conditions is carried out between foot-mounted and belt-mounted techniques given an inertial measurement unit (IMU) commercialized by XSens.
Abstract
The interest in location based services is growing in several applications. The literature exhibits a wide spectrum of technology to complement the well-knwon limitations of satellite based positioning systems in constrained environments such as indoors or urban canyons. This paper focuses on inertial sensors and systems to locate pedestrians indoor without infrastructure. The theoretical background of a recently developped belt-mounted inertial navigation system (INS) is carefully depicted here. The approach aims to facilitate the equipment and the mobility of the users while maintaining repeatable performance. Therefore, a comparison of the performance in realistic conditions is carried out between foot-mounted and belt-mounted techniques given an inertial measurement unit (IMU) commercialized by XSens. Then, this commercial IMU and an IMU based on ADXL345 and ITG3200 were compared, given the belt-mounted algorithm, in terms of positioning performance. The results, supported by dozens of experiments involving different participants, show that the belt-mounted technique is as efficient as the foot-mounted one since the average error in position is less than 2% of the travelled distance about 200m. Whereas their costs are very different, the commercial and the integrated IMU reach a similar accuracy. The belt-mounted device achieve repeatable and efficient pedestrian indoor positioning in real-time with low-cost inertial sensors.

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Citations
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Foot-mounted Pedestrian Navigation based on Particle Filter with an Adaptive Weight Updating Strategy

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A HMM map-matching approach enhancing indoor positioning performances of an inertial measurement system

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

TL;DR: A novel approach is developed for quasi-static magnetic field detection in foot-mounted Inertial Navigation System and can provide superior performance in suppressing the heading errors with the comparison to the iQSF method.
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Attention-Based Event Characterization for Scarce Vehicular Sensing Data

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References
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Journal ArticleDOI

A Survey of Indoor Inertial Positioning Systems for Pedestrians

TL;DR: It is concluded that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours.
Proceedings ArticleDOI

Foot-mounted INS for everybody - an open-source embedded implementation

TL;DR: An open-source, realtime, embedded implementation of a foot-mounted, zero-velocity-update-aided inertial navigation system that uses off-the-shelf components and assembly methods, and features a standard USB interface.
Journal ArticleDOI

Geometric Integration of Quaternions

TL;DR: A geometric integration algorithm is employed to propagate the quaternion kinematics in order to preserve the unit norm and it is found that the fourth order Crouch-Grossman algorithm is more accurate than its Runge-Kutta counterpart except for the smallest time step used.
Proceedings ArticleDOI

A study on indoor pedestrian localization algorithms with foot-mounted sensors

TL;DR: The work presents a foot-mounted sensor system for a combined indoor/outdoor pedestrian localization based on a zero-velocity update scheme formulated as an Extended or Unscented Kalman filter with quaternion orientation representation and employs a custom low-cost sensor unit.
Proceedings ArticleDOI

Geometric Integration of Quaternions

TL;DR: In this article, the authors employ a geometric integration algorithm to propagate the quaternion kinematics in order to preserve the unit norm, and compare the results obtained using third and fourth order Crouch-Grossman Lie group methods with those calculated using the classical third-and fourth-order Runge-Kutta algorithms using different time steps.