A Hybrid of Smartphone Camera and Basestation Wide-area Indoor Positioning Method = A Hybrid of Smartphone Camera and Basestation Wide-area Indoor Positioning Method

다국어 초록 (Multilingual 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 (1σ) 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.

참고문헌 (Reference)

1 Y. Benezeth., "Vision-based system for human detection and tracking in indoor environment" 2 : 41-52, 2010

2 M. Muñoz-Organero., "Using bluetooth to implement a pervasive indoor positioning system with minimal requirements at the application level" 8 : 73-82, 2012

3 M. C. Gonzalez., "Understanding individual human mobility patterns" 453 : 779-782, 2008

4 M. J. Kuhn., "Ultra wideband 3-D tracking of multiple tags for indoor positioning in medical applications requiring millimeter accuracy" 57-60, 2012

5 H. Liu., "Survey of wireless indoor positioning techniques and systems" IEEE Transactions on 37 : 1067-1080, 2007

6 R. Mautz., "Survey of optical indoor positioning systems" 1-7, 2011

7 H. Bay., "Surf : Speeded up robust features" Springer 404-417, 2006

8 D. Zhongliang., "Situation and development tendency of indoor positioning" 10 : 42-55, 2013

9 Valgren, Christoffer., "SIFT, SURF and Seasons : Long-term Outdoor Localization Using Local Features" 2007

10 A. Bekkali., "RFID indoor positioning based on probabilistic RFID map and Kalman filtering" 21-21, 2007

11 S. Mazuelas., "Prior NLOS measurement correction for positioning in cellular wireless networks" 58 : 2585-2591, 2009

12 E. Rosten., "Machine learning for high-speed corner detection" Springer 430-443, 2006

13 J. Barnes., "Locata : A new positioning technology for high precision indoor and outdoor positioning" 9-18, 2003

14 L. -H. Chen., "Intelligent fusion of Wi-Fi and inertial sensor-based positioning systems for indoor pedestrian navigation" 14 : 4034-4042, 2014

15 L. -H. Chen., "Intelligent fusion of Wi-Fi and inertial sensor-based positioning systems for indoor pedestrian navigation" IEEE 14 : 4034-4042, 2014

16 Li, Xun., "Image matching techniques for vision-based indoor navigation systems : performance analysis for 3D map based approach" 2012

17 J. Z. Liang., "Image based localization in indoor environments" 70-75, 2013

18 Z. Tian., "Fingerprint indoor positioning algorithm based on affinity propagation clustering" 2013 : 1-8, 2013

19 Y. Cui., "Autonomous vehicle positioning with GPS in urban canyon environments" 19 : 15-25, 2003

20 S. -H. Fang., "An enhanced ZigBee indoor positioning system with an ensemble approach" IEEE 16 : 564-567, 2012

21 F. Evennou., "Advanced integration of WiFi and inertial navigation systems for indoor mobile positioning" 2006 : 164-164, 2006

22 Y. Gu., "A survey of indoor positioning systems for wireless personal networks" 11 : 13-32, 2009

23 J. Wang., "A study on wireless sensor network based indoor positioning systems for context‐aware applications" 12 : 53-70, 2012

24 J. Wang., "A study on wireless sensor network based indoor positioning systems for context‐aware applications" 12 : 53-70, 2012

25 C. Chen., "A RGB and D vision aided multi-sensor system for indoor mobile robot and pedestrian seamless navigation" 1020-1025, 2014