Paper Info
Title
A Hybrid of Smartphone Camera and Basestation Wide-area Indoor Positioning Method.
Abstract
Indoor positioning is considered an enabler for a variety of applications, the demand for an indoor positioning service has also been accelerated. That is because that people spend most of their time indoor environment. Meanwhile, the smartphone integrated powerful camera is an efficient platform for navigation and positioning. However, for high accuracy indoor positioning by using a smartphone, there are two constraints that includes: (1) limited computational and memory resources of smartphone; (2) users' moving in large buildings. To address those issues, this paper uses the TC-OFDM for calculating the coarse positioning information includes horizontal and altitude information for assisting smartphone camera-based positioning. Moreover, a unified representation model of image features under variety of scenarios whose name is FAST-SURF is established for computing the fine location. Finally, an optimization marginalized particle filter is proposed for fusing the positioning information from TC-OFDM and images. The experimental result shows that the wide location detection accuracy is 0.823 m (1 sigma) at horizontal and 0.5 m at vertical. Comparing to the WiFi-based and ibeacon-based positioning methods, our method is powerful while being easy to be deployed and optimized.
Year
DOI
Venue
2016
10.3837/tiis.2016.02.016
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
Keywords
Field
DocType
Indoor positioning,smartphone camera,fusion of telecommunication and navigation,image feature descriptor,marginalized particle filter
Hybrid positioning system,Computer vision,Location detection,Computer science,Feature (computer vision),Particle filter,iBeacon,Artificial intelligence,Precise Point Positioning
Journal
Volume
Issue
ISSN
10
2
1976-7277
Citations 
PageRank 
References 
2
0.36
0
Authors
4
Name
Order
Citations
PageRank
Jichao Jiao1186.53
Zhongliang Deng2217.47
Lianming Xu320.36
Fei Li49739.93