Precise indoor localization with 3D facility scan data
Precise indoor localization with 3D facility scan data
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DOI：
10.1111/mice.12795
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Jiahao Xia;Jie Gong
中科院分区：
文献类型：
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作者：
Jiahao Xia;Jie Gong
随着智能手机上普遍配备摄像头，智能室内服务的视觉室内定位越来越受到人们的关注。在本研究中，基于 3D 设施扫描数据设计并验证了分层室内定位算法，这些数据最初是为了设施建模目的而收集的。该研究在室内定位方面取得了有希望的结果。该研究还展示了一种可扩展的方法，可以根据激光扫描数据的参考姿势生成高质量图像，为生成标记图像以训练端到端姿势回归模型（即 PoseNet）打开了大门。在这方面，本研究是利用设施扫描数据进行室内定位的首次尝试，这些数据通常是为建筑信息模型 (BIM) 目的而收集的。随着越来越多的设施使用激光扫描仪进行记录，我们的算法可以为智能应用解锁所收集数据的附加价值。
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.