Indoor pedestrian localisation solution based on anemometry sensor integration with a smartphone

Abstract

The paper deals with the design, calibration and experimental validation of a novel infrastructure-less solution dedicated to indoor pedestrian localisation issues. The approach involves aerodynamic fluid computation for instantaneous speed estimation of a pedestrian handling a smartphone. For this purpose, a differential pressure-based MEMS anemometer is integrated to an Android smartphone by means of a dedicated PIC 32 bits microcontroller. Measurements of the pedestrian orientation are ensured by a gyroscope sensor coupled with the smartphone. Consequently, both instantaneous speed and heading measurements are combined to the dead reckoning technique for estimating the 2D relative position of the user. Theoretical modeling is conducted in order to calibrate and quantify the accuracy of the sensor. In situ experiments along straight paths demonstrate that the sensors coupled with a smartphone achieve pedestrian localisation with average accuracy of less than 6 % of the total travelled distance.Index Terms-Indoor pedestrian localisation, infrastructure less solution, sensor augmented smartphone, localisation based service, smart sensors.

Localization in terrestrial and underwater sensor‐based m2m communication networks: architecture,classification and challenges

“…To alleviate these problems and improve localization accuracy, radio frequency identification (RFID) technology can be used to localize sensors. Hence, it reduces the effect of magnetic field disturbance, whereas other approaches [51] directly obtain such information from smart phones or integrate anemometer and suffers from magnetic field disturbance. The work performed in [50] introduces an indoor localization approach that uses smart phone integrated with sensors.…”

“…This approach uses a modified Kalman filter-based data fusion to estimate the orientation and gait length. Hence, it reduces the effect of magnetic field disturbance, whereas other approaches [51] directly obtain such information from smart phones or integrate anemometer and suffers from magnetic field disturbance.…”

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“…20 Advances in this context could come from the use of suitable data fusion techniques, often based on extended Kalman filters, [21][22][23] aimed to improve performance of the navigation system. 20 Concerning technologies, different solutions have been proposed, eg, standalone customized devices, [24][25][26] smartphone-based platforms, 27,28 and mixed systems. 29,30 Although smartphone-based assistive systems could discourage frail people, considering that the cultural heritage in information and communication technology systems is rapidly spreading, such solutions can be very convenient for the safe and efficient exploitation of educational/cultural/job environments.…”

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“…is the rotation matrix associated to the error on angle estimation: Berr = ow.�t (4) where Ow is the angular rate bias and �t the integration step or inverse of sampling frequency. Impact of angular errors on acceleration projection and distance estimation.…”

“…At this point, the technology we chose to focus on should be infrastructure less and possibly embedded in a smartphone, in order to target the mass market. The literature relates a variety of pedestrian positioning solu tions including pressure changes due to altitude modification between floors in a building [3], draughts induced by displace ment [4], magnetic perturbations characterizing a location [5] or mechanical sensors measuring body moves [6]. Another constraint to take into account concerns environ mental dependency.…”