TY - JOUR
T1 - Indoor Quality-of-Position Visual Assessment Using Crowdsourced Fingerprint Maps
AU - Laoudias, Christos
AU - Nikitin, Artyom
AU - Karras, Panagiotis
AU - Youssef, Moustafa
AU - Zeinalipour-Yazti, Demetrios
PY - 2021/2
Y1 - 2021/2
N2 - Internet-based Indoor Navigation (IIN) architectures organize signals collected by crowdsourcers in Fingerprint Maps (FMs) to improve localization given that satellite-based technologies do not operate accurately in indoor spaces where people spend 80%–90% of their time. In this article, we study the Quality-of-Position (QoP) assessment problem, which aims to assess in an offline manner the localization accuracy that can be obtained by a user that aims to localize using a FM. Particularly, our proposed ACCES framework uses a generic interpolation method using Gaussian Processes (GP), upon which a navigability score at any location is derived using the Cramer-Rao Lower Bound (CRLB). We derive adaptations of ACCES for both Magnetic and Wi-Fi data and implement a complete visual assessment environment, which has been incorporated in the Anyplace open-source IIN. Our experimental evaluation of ACCES in Anyplace suggests the high qualitative and quantitative benefits of our propositions.
AB - Internet-based Indoor Navigation (IIN) architectures organize signals collected by crowdsourcers in Fingerprint Maps (FMs) to improve localization given that satellite-based technologies do not operate accurately in indoor spaces where people spend 80%–90% of their time. In this article, we study the Quality-of-Position (QoP) assessment problem, which aims to assess in an offline manner the localization accuracy that can be obtained by a user that aims to localize using a FM. Particularly, our proposed ACCES framework uses a generic interpolation method using Gaussian Processes (GP), upon which a navigability score at any location is derived using the Cramer-Rao Lower Bound (CRLB). We derive adaptations of ACCES for both Magnetic and Wi-Fi data and implement a complete visual assessment environment, which has been incorporated in the Anyplace open-source IIN. Our experimental evaluation of ACCES in Anyplace suggests the high qualitative and quantitative benefits of our propositions.
KW - Indoor localization
KW - fingerprint management
KW - accuracy estimation
UR - http://www.scopus.com/inward/record.url?scp=85100840285&partnerID=8YFLogxK
U2 - 10.1145/3433026
DO - 10.1145/3433026
M3 - Journal article
SN - 2374-0353
VL - 7
JO - ACM Transactions on Spatial Algorithms and Systems
JF - ACM Transactions on Spatial Algorithms and Systems
IS - 2
M1 - 10
ER -