CloudNavi: Toward Ubiquitous Indoor Navigation Service with 3D Point Clouds

被引:17
作者
Teng, Xiaoqiang [1 ]
Guo, Deke [1 ,2 ]
Guo, Yulan [3 ,4 ]
Zhou, Xiaolei [5 ]
Liu, Zhong [1 ]
机构
[1] Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Hunan, Peoples R China
[2] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China
[3] Natl Univ Def Technol, Coll Elect Sci, Changsha 410073, Hunan, Peoples R China
[4] Sun Yat Sen Univ, Sch Elect & Commun Engn, Guangzhou 510275, Guangdong, Peoples R China
[5] Natl Univ Def Technol, 63 Res Inst, Nanjing 210089, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Indoor navigation; point cloud processing; mobile crowdsourcing; 3D path-map; indoor localization; LOCALIZATION; ACCURATE;
D O I
10.1145/3216722
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The rapid development of mobile computing has prompted indoor navigation to be one of the most attractive and promising applications. Conventional designs of indoor navigation systems depend on either infrastructures or indoor floor maps. This article presents CloudNavi. a ubiquitous indoor navigation solution, which relies on the point clouds acquired by the 3D camera embedded in a mobile device. Particularly, CloudNavi first efficiently infers the walking trace of each user from captured point clouds and inertial data. Many shared walking traces and associated point clouds are combined to generate the point cloud traces, which are then used to generate a 3D path-map. Accordingly, CloudNavi can accurately estimate the location of a user by fusing point clouds and inertial data using a particle filter algorithm and then guiding the user to its destination from its current location. Extensive experiments are conducted on office building and shopping mall datasets. Experimental results indicate that CloudNavi exhibits outstanding navigation performance in both office buildings and shopping malls and obtains around 34% improvement compared with the state-of-the-art method.
引用
收藏
页数:28
相关论文
共 50 条
[31]   Optimizing Wireless Sensor Network Installations by Visibility Analysis on 3D Point Clouds [J].
Gracchi, Teresa ;
Gigli, Giovanni ;
Noel, Francois ;
Jaboyedoff, Michel ;
Madiai, Claudia ;
Casagli, Nicola .
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019, 8 (10)
[32]   Smart Phone Based Sensor Fusion by Using Madgwick Filter for 3D Indoor Navigation [J].
Md. Abid Hasan ;
Md. Hafizur Rahman .
Wireless Personal Communications, 2020, 113 :2499-2517
[33]   3D Geometry-Based Indoor Network Extraction for Navigation Applications Using SFCGAL [J].
Tekavec, Jernej ;
Lisec, Anka .
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2020, 9 (07)
[34]   Smart Phone Based Sensor Fusion by Using Madgwick Filter for 3D Indoor Navigation [J].
Hasan, Md Abid ;
Rahman, Md Hafizur .
WIRELESS PERSONAL COMMUNICATIONS, 2020, 113 (04) :2499-2517
[35]   Automated 3D Wireframe Modeling of Indoor Structures from Point Clouds Using Constrained Least-Squares Adjustment for As-Built BIM [J].
Jung, Jaehoon ;
Hong, Sungchul ;
Yoon, Sanghyun ;
Kim, Jeonghyun ;
Heo, Joon .
JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2016, 30 (04)
[36]   Point cloud room segmentation based on indoor spaces and 3D mathematical morphology [J].
Frias, E. ;
Balado, J. ;
Diaz-Vilarino, L. ;
Lorenzo, H. .
ISPRS TC IV 3RD BIM/GIS INTEGRATION WORKSHOP AND 15TH 3D GEOINFO CONFERENCE 2020, 2020, 44-4 (W1) :49-55
[37]   Leveraging 3D Multipath Projection in Indoor Localization System with a Single Access Point [J].
Huang, Chenglin ;
He, Wei ;
Tian, Zengshan ;
Liu, Kaikai .
2022 IEEE 10TH ASIA-PACIFIC CONFERENCE ON ANTENNAS AND PROPAGATION, APCAP, 2022,
[38]   Effective Indoor Localization and 3D Point Registration Based on Plane Matching Initialization [J].
Zhu, Dongchen ;
Xing, Ziran ;
Li, Jiamao ;
Gu, Yuzhang ;
Zhang, Xiaolin .
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2017, E100D (06) :1316-1324
[39]   Application of 3D point cloud map and image identification to mobile robot navigation [J].
Lin, Tsung-Ying ;
Juang, Jih-Gau .
MEASUREMENT & CONTROL, 2023, 56 (5-6) :911-927
[40]   Adaptive Lidar Scan Frame Integration: Tracking Known MAVs in 3D Point Clouds [J].
Li Qingqing ;
Yu Xianjia ;
Queralta, Jorge Pena ;
Westerlund, Tomi .
2021 20TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS (ICAR), 2021, :1079-1086