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Perspective Projection Algorithm Enabling Mobile Device’s Indoor Positioning

DOI: 10.4236/jcc.2018.61017, PP. 159-170

Keywords: Camera Pose Estimation, Indoor Positioning, Perspective Projection, Homography

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In order to improve the user’s satisfaction with the augmented reality (AR) technology and the accuracy of the service, it is important to obtain the exact position of the user. Frequently used techniques for finding outdoors locations is the global positioning system (GPS), which is less accurate indoors. Therefore, an indoor position is measured by comparing the reception level about access point (AP) signal of wireless fidelity (Wi-Fi) or using bluetooth low energy (BLE) tags. However, Wi-Fi and Bluetooth require additional hardware installation. In this paper, the proposed method of estimating the user’s position uses an indoor image and indoor coordinate map without additional hardware installation. The indoor image has several feature points extracted from fixed objects. By matching the feature points with the feature points of the user image, we can obtain the position of the user on the Indoor map by obtaining six or more pixel coordinates from the user image and solving the solution using the perspective projection formula. The experimental results show that the user position can be obtained more accurately in the indoor environment by using only the software without additional hardware installation.


[1]  Grillo, G., Lamberti, F., Manuri, F., Sanna, A., Paravati, G., Pezzolla, P. and Montuschi, P. (2014) Challenges, Opportunities, and Future Trends of Emerging Techniques for Augmented Reality-Based Maintenance. Transactions on Emerging Topics in Computing, 2, 411-421.
[2]  Mahiddin, N.A., Madi, E.N., Dhalila, S., Hasan, E.F., Safie, S. and Safie, N. (2013) User Position Detection In an Indoor Environment. International Journal of Multimedia and Ubiquitous Engineering, 8, 303-312.
[3]  Dinh-Van, N., Nashashibi, F., Thanh-Huong, N. and Castelli, E. (2017) Indoor Intelligent Vehicle localization Using WiFi Received Signal Strength Indicator. 2017 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), Nagoya, 33-36.
[4]  Kalbandhe, A.A. and Patil, S.C. (2016) Indoor Positioning System Using Bluetooth Low Energy. 2016 International Conference on Computing, Analytics and Security Trends (CAST), Pune, 451-455.
[5]  Soewito, B., Faahakhododo, I. and Gunawan, F.E. (2016) Increasing the Accuracy of Distance Measurement between Access Point and Smartphone. 2016 11th International Conference on Knowledge, Information and Creativity Support Systems (KICSS), Yogyakarta, 1-6.
[6]  Marsh, D. (2005) Applied Geometry for Computer Graphics and CAD. 2nd Edition.
[7]  Jeon, S.J., Kim, J., Ji, M.G., Park, J.H. and Choi, Y.W. (2017) Position Error Correction Using Homography in Discretized Positioning Circuit for Gamma-Ray Imaging Detection System. IEEE Transactions on Nuclear Science, 64, 816-819.
[8]  Bay, H., Tuytelaars, T. and Van Gool, L. (2006) Surf: Speeded up Robust Features. Computer Vision-ECCV 2006, 404-417.
[9]  Fischler, M.A. and Bolles, R.C. (1981) Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24, 381-395.
[10]  Hartley, R. and Zisserman, A. (2000) Multiple View Geometry in Computer Vision. 2nd Edition, Cambridge University Press, Cambridge.


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