Paper Info
Title
Copernicus: A Robust AI-Centric Indoor Positioning System
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.
Year
DOI
Venue
2018
10.1109/IPIN.2018.8533821
2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
Keywords
Field
DocType
production deployments,adaptive system,robust AI-centric indoor positioning system,Bluetooth low energy beacons,BLE beacons,self-learning system,sensor cost,specific indoor area,smartphone model,Copernicus
Beacon,Software deployment,Adaptive system,Particle filter,Real-time computing,Electronic engineering,Copernicus,Engineering,Standardization,Indoor positioning system,Bluetooth Low Energy
Conference
ISSN
ISBN
Citations 
2162-7347
978-1-5386-5636-5
0
PageRank 
References 
Authors
0.34
10
2
Name
Order
Citations
PageRank
Yiannis Gkoufas1175.96
Kostas Katrinis210219.41