Proceedings ArticleDOI
Copernicus: A Robust AI-Centric Indoor Positioning System
Yiannis Gkoufas,Kostas Katrinis +1 more
- pp 206-212
TLDR
Copernicus is proposed, a self-learning, adaptive system that is shown to exhibit improved accuracy across different smartphone models, and leverages a minimal deployment of Bluetooth Low Energy Beacons to infer the trips of users, learn and eventually build tailored Magnetic Maps for every smartphone model for the specific indoor area.Abstract:
Indoor Positioning Systems (IPS) are gaining market momentum, mainly due to the significant reduction of sensor cost (on smartphones or standalone) and leveraging standardization of related technology. Among various alternatives for accurate and cost-effective IPS, positioning based on the Magnetic Field has proven popular, as it does not require specialized infrastructure. Related experimental results have demonstrated good positioning accuracy. However, when transitioned to production deployments, these systems exhibit serious drawbacks to make them practical: a) accuracy fluctuates significantly across smartphone models and configurations and b) costly continuous manual fingerprinting of the area is required. In this paper we propose Copernicus, a self-learning, adaptive system that is shown to exhibit improved accuracy across different smartphone models. Copernicus leverages a minimal deployment of Bluetooth Low Energy (BLE) Beacons to infer the trips of users, learn and eventually build tailored Magnetic Maps for every smartphone model for the specific indoor area. Our experimental results show the positive impact in the positioning, even in case of minimal learning.read more
Citations
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Journal ArticleDOI
Survey of Machine Learning Methods Applied to Urban Mobility
TL;DR: This work presents a survey on urban mobility based on passengers’ data and machine learning methods, focusing on four applications for urban mobility: public datasets, passenger localization, detection of the transport mode and pattern recognition and generation of mobility models.
Proceedings ArticleDOI
Anatomy and Deployment of Robust AI-Centric Indoor Positioning System
Yiannis Gkoufas,Stefano Braghin +1 more
TL;DR: The developed Indoor Positioning System Copernicus is a self-learning, adaptive system that is shown to exhibit improved accuracy across different smartphone models, and leverages a minimal deployment of Bluetooth Low Energy Beacons to infer the trips of users, and eventually build tailored Magnetic Maps for every smartphone model for the specific indoor area.
References
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Journal ArticleDOI
Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons
Suining He,S.-H. Gary Chan +1 more
TL;DR: This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment.
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.
Journal ArticleDOI
Location Fingerprinting With Bluetooth Low Energy Beacons
Ramsey Faragher,Robert Harle +1 more
TL;DR: This work provides a detailed study of BLE fingerprinting using 19 beacons distributed around a ~600 m2 testbed to position a consumer device, and investigates the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency.
Proceedings Article
Decimeter-level localization with a single WiFi access point
TL;DR: Chronos, a system that enables a single WiFi access point to localize clients to within tens of centimeters, demonstrates that Chronos's accuracy is comparable to state-of-the-art localization systems, which use four or five access points.
Proceedings ArticleDOI
Indoor location sensing using geo-magnetism
TL;DR: An indoor positioning system that measures location using disturbances of the Earth's magnetic field caused by structural steel elements in a building that demonstrates accuracy within 1 meter 88% of the time in experiments in two buildings and across multiple floors within the buildings.
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