TY - JOUR
T1 - Precise indoor localization with 3D facility scan data
AU - Xia, Jiahao
AU - Gong, Jie
N1 - Funding Information:
informationUS National Science Foundation, Grant/Award Numbers: 1827505 and 1737533This research project was performed by Rutgers, The State University of New Jersey, and was sponsored by US National Science Foundation under awards 1827505 and 1737533. The funding and support from US National Science Foundation are appreciated.
Publisher Copyright:
© 2021 Computer-Aided Civil and Infrastructure Engineering.
PY - 2022/8
Y1 - 2022/8
N2 - Visual indoor localization for smart indoor services is a growing field of interest as cameras are now ubiquitously equipped on smartphones. In this study, a hierarchical indoor localization algorithm is designed and validated based on 3D facility scan data, which are originally collected for facility modeling purposes. The study has shown promising results in indoor localization. The study also demonstrated a scalable approach to generate high-quality images with reference poses from laser scan data, opening doors to generate labeled images to train end-to-end pose regression model (i.e., PoseNet). In this regard, this study is the first attempt to leverage facility scan data, which are commonly collected for Building Information Modeling (BIM) purpose, for indoor localization. As more facilities are documented with laser scanners, our algorithm can unlock additional values of collected data for intelligent applications.
AB - Visual indoor localization for smart indoor services is a growing field of interest as cameras are now ubiquitously equipped on smartphones. In this study, a hierarchical indoor localization algorithm is designed and validated based on 3D facility scan data, which are originally collected for facility modeling purposes. The study has shown promising results in indoor localization. The study also demonstrated a scalable approach to generate high-quality images with reference poses from laser scan data, opening doors to generate labeled images to train end-to-end pose regression model (i.e., PoseNet). In this regard, this study is the first attempt to leverage facility scan data, which are commonly collected for Building Information Modeling (BIM) purpose, for indoor localization. As more facilities are documented with laser scanners, our algorithm can unlock additional values of collected data for intelligent applications.
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U2 - 10.1111/mice.12795
DO - 10.1111/mice.12795
M3 - Article
AN - SCOPUS:85119199678
SN - 1093-9687
VL - 37
SP - 1243
EP - 1259
JO - Computer-Aided Civil and Infrastructure Engineering
JF - Computer-Aided Civil and Infrastructure Engineering
IS - 10
ER -