ACCES: Offline Accuracy Estimation for Fingerprint-Based Localization

Publikation: Forskning - peer reviewKonferenceartikel i proceeding

  • Artyom Nikitin
  • Christos Laoudias
  • Georgios Chatzimilioudis
  • Panagiotis Karras
  • Demetrios Zeinalipour-Yazti

Abstrakt

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.
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Detaljer

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.
OriginalsprogEngelsk
Titel18th IEEE International Conference on Mobile Data Management (MDM)
Antal sider2
ForlagIEEE
Publikationsdato30 maj 2017
StatusUdgivet - 30 maj 2017
PublikationsartForskning
Peer reviewJa
Begivenhed18th IEEE International Conference on Mobile Data Management - KAIST, Daejeon, Sydkorea
Varighed: 29 maj 20171 jun. 2017
Konferencens nummer: 18
http://mdmconferences.org/mdm2017/

Konference

Konference18th IEEE International Conference on Mobile Data Management
Nummer18
LokationKAIST
LandSydkorea
ByDaejeon
Periode29/05/201701/06/2017
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