Pedestrian Indoor Localization Using Foot Mounted Inertial Sensors in Combination with a Magnetometer, a Barometer and RFID

Romanovas, Michailas; Goridko, Vadim; Klingbeil, Lasse; Bourouah, Mohamed; Al-Jawad, Ahmed; Traechtler, Martin; Manoli, Yiannos

doi:10.1007/978-3-642-34203-5_9

Cited by 14 publications

(6 citation statements)

References 17 publications

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“…Indoor positioning for pedestrian navigation is still an open problem as no positioning system that delivers absolute -such as GPS -coordinates is available. Many solutions providing precise (sub meter) localisation require additional technical devices (Guo et al 2015;Pham and Suh 2016;Romanovas et al 2013). However, they are not at disposal in everyday life situations at which pedestrian navigation systems target.…”

Section: Introductionmentioning

confidence: 99%

Exploring the limits of PDR-based indoor localisation systems under realistic conditions

Jackermeier

Ludwig

2018

Journal of Location Based Services

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Pedestrian Dead Reckoning (PDR) plays an important role in many (hybrid) indoor positioning systems since it enables frequent, granular position updates. However, the accumulation of errors creates a need for external error correction. In this work, we explore the limits of PDR under realistic conditions using our graph-based system as an example. For this purpose, we collect sensor data while the user performs an actual navigation task using a navigation application on a smartphone. To assess the localisation performance, we introduce a task-oriented metric based on the idea of landmark navigation: instead of specifying the error metrically, we measure the ability to determine the correct segment of an indoor route, which in turn enables the navigation system to give correct instructions. We conduct offline simulations with the collected data in order to identify situations where position tracking fails and explore different options how to mitigate the issues, e.g. through detection of special features along the user's path or through additional sensors. Our results show that the magnetic compass is often unreliable under realistic conditions and that resetting the position at strategically chosen decision points significantly improves positioning accuracy.

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Section: Introductionmentioning

confidence: 99%

Exploring the limits of PDR-based indoor localisation systems under realistic conditions

Jackermeier

Ludwig

2018

Journal of Location Based Services

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“…The motivation of the paper is to further extend the method presented in [ 17 ] and tackle human position estimation over long duration and distance. Although the incorporation of extra infrastructures, such as ultrasound, short-range radio (Wi-Fi, Ultra-Wideband (UWB), radio frequency identification (RFID) and Zigbee) or vision [ 20 , 21 ], can improve the tracking accuracy for long distance scenarios, the extra instrumentations make the systems less ubiquitous in terms of installation and maintenance. However, instead of using extra infrastructures, other information, such as map information can also be acquired in advance and used for human position estimation [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking

Bao

Meng

Xiao

et al. 2017

Sensors

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Abstract:The wearable inertial/magnetic sensor based human motion analysis plays an important role in many biomedical applications, such as physical therapy, gait analysis and rehabilitation. One of the main challenges for the lower body bio-motion analysis is how to reliably provide position estimations of human subject during walking. In this paper, we propose a particle filter based human position estimation method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity update (ZUPT), but also applies map information to further correct the acceleration double integration drift and thus improve estimation accuracy. In the proposed method, a simple stance phase detector is designed to identify the stance phase of a gait cycle based on gyroscope measurements. For the non-stance phase during a gait cycle, an acceleration control variable derived from ZUPT information is introduced in the process model, while vector map information is taken as binary pseudo-measurements to further enhance position estimation accuracy and reduce uncertainty of walking trajectories. A particle filter is then designed to fuse ZUPT information and binary pseudo-measurements together. The proposed human position estimation method has been evaluated with closed-loop walking experiments in indoor and outdoor environments. Results of comparison study have illustrated the effectiveness of the proposed method for application scenarios with useful map information.

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“…MEMS-barometers are cheap and useful sensors to be implemented into the MEMS-IMU positioning system [ 30 ]. However, the barometer output is heavily influenced by the environment [ 31 , 32 ]. In order to solve this problem, a height difference information-aided barometer (HDIB) algorithm is developed.…”

Section: Introductionmentioning

confidence: 99%

A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System

Yang

Xiu

Zhang

et al. 2017

Sensors

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In this paper, we present a novel method for 3D pedestrian navigation of foot-mounted inertial systems by integrating a MEMS-IMU, barometer, and permanent magnet. Zero-velocity update (ZUPT) is a well-known algorithm to eliminate the accumulated error of foot-mounted inertial systems. However, the ZUPT stance phase detector using acceleration and angular rate is threshold-based, which may cause incorrect stance phase estimation in the running gait pattern. A permanent magnet-based ZUPT detector is introduced to solve this problem. Peaks extracted from the magnetic field strength waveform are mid-stances of stance phases. A model of peak-peak information and stance phase duration is developed to have a quantitative calculation method of stance phase duration in different movement patterns. Height estimation using barometer is susceptible to the environment. A height difference information aided barometer (HDIB) algorithm integrating MEMS-IMU and barometer is raised to have a better height estimation. The first stage of HDIB is to distinguish level ground/upstairs/downstairs and the second stage is to calculate height using reference atmospheric pressure obtained from the first stage. At last, a ZUPT-based adaptive average window length algorithm (ZUPT-AAWL) is proposed to calculate the true total travelled distance to have a more accurate percentage error (TTDE). This proposed method is verified via multiple experiments. Numerical results show that TTDE ranges from 0.32% to 1.04% in both walking and running gait patterns, and the height estimation error is from 0 m to 2.35 m.

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Pedestrian Indoor Localization Using Foot Mounted Inertial Sensors in Combination with a Magnetometer, a Barometer and RFID

Cited by 14 publications

References 17 publications

Exploring the limits of PDR-based indoor localisation systems under realistic conditions

Exploring the limits of PDR-based indoor localisation systems under realistic conditions

Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking

A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System

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