Abstract
In this demonstration we present ACCES, a novel framework that enables quality assessment of arbitrary fingerprint maps and offline accuracy estimation for the task of fingerprint-based indoor localization. Our framework considers collected fingerprints disregarding the physical origin of the data. First, it applies a widely used statistical instrument, namely Gaussian Process Regression (GPR), for interpolation of the fingerprints. Then, to estimate the best possibly achievable localization accuracy at any location, it utilizes the Cramer-Rao Lower Bound (CRLB) with interpolated data as an input. Our demonstration entails a standalone version of the popular and open-source Anyplace Internet-based indoor navigation service in which the software modules of ACCES are integrated. At the conference, we will present the utility of our method in two modes: (i) Collection Mode, where attendees will be able to use our service directly to collect signal measurements over the venue using an Android smartphone; and (ii) Reflection Mode, where attendees will be able to observe the collected measurements and the respective ACCES accuracy estimations in the form of an overlay heatmap.
Originalsprog | Engelsk |
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Titel | 18th IEEE International Conference on Mobile Data Management (MDM) |
Antal sider | 2 |
Forlag | IEEE |
Publikationsdato | 30 maj 2017 |
Sider | 358-359 |
ISBN (Elektronisk) | 978-1-5386-3932-0 |
DOI | |
Status | Udgivet - 30 maj 2017 |
Begivenhed | 18th IEEE International Conference on Mobile Data Management - KAIST, Daejeon, Sydkorea Varighed: 29 maj 2017 → 1 jun. 2017 Konferencens nummer: 18 http://mdmconferences.org/mdm2017/ |
Konference
Konference | 18th IEEE International Conference on Mobile Data Management |
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Nummer | 18 |
Lokation | KAIST |
Land/Område | Sydkorea |
By | Daejeon |
Periode | 29/05/2017 → 01/06/2017 |
Internetadresse |
Navn | IEEE International Conference on Mobile Data Management (MDM) |
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ISSN | 2375-0324 |