|Search by item||HOME > Access full text > Search by item|
JBE, vol. 24, no. 1, pp.32-40, January, 2019
Algorithm to Improve Accuracy of Location Estimation for AR Games
Seo Woo Han and Doug Young Suh
C.A E-mail: email@example.com
Indoor location estimation studies are needed in various fields. The method of estimating the indoor position can be divided into a method using hardware and a method using no hardware. The use of hardware is more accurate, but has the disadvantage of hardware installation costs. Conversely, the non-hardware method is not costly, but it is less accurate. To estimate the location for AR game, you need to get the solution of the Perspective-N-Point (PnP). To obtain the PnP problem, we need three-dimensional coordinates of the space in which we want to estimate the position and images taken in that space. The position can be estimated through six pairs of two-dimensional coordinates matching the three-dimensional coordinates. To further increase the accuracy of the solution, we proposed the use of an additional non-coplanarity degree to determine which points would increase accuracy. As the non-coplanarity degree increases, the accuracy of the position estimation becomes higher. The advantage of the proposed method is that it can be applied to all existing location estimation methods and that it has higher accuracy than hardware estimation.
Keyword: non-coplanarity, AR game, pose estimation, perspective projection, point select
 F. Wei, L. Zheng, H. Deng, and H. Zhang, “Real-time motion tracking for mobile augmented/virtual reality using adaptive visual-inertial fusion,” Sensors Vol.17, No. 5, p.1037, May 2017, doi: 10.3390/ s17051037.
 Y. Ma, X. Wu, G. Yu, Y. Xu, and Y. Wang, “Pedestrian detection and tracking from low-resolution unmanned aerial vehicle thermal imagery,”
Sensors, Vol.16, No. 4, p.446, March 2016, doi: 10.3390/ s16040446.  Ming, M, Song, Q, Y. Gu and Z. Zhou, “Use of Magnetic Field for Mitigating Gyroscope Errors for Indoor Pedestrian Positioning,” Sensors, Vol.18, No.8, p.2592, August 2018, doi: 10.3390/s18082592.
 J. Bang, D. Lee, Y. Kim and H. Lee, “Camera Pose Estimation Using Optical Flow and ORB Descriptor in SLAM-Based Mobile AR Game,” 2017
International Conference on Platform Technology and Service (PlatCon), Busan, Korea, pp.1-4, 2017, doi: 10.1109/PlatCon.2017. 7883693.  Y. Alexander, N. Lo and D. Winata, “An Indoor Positioning-Based Mobile Payment System Using Bluetooth Low Energy Technology,” Sensors, Vol.18, No.4, p.974, March 2018, doi: 10.3390/s18040974.
 S. Benfano, I. Faahakhododo and F. Gunawan, “Increasing the accuracy of distance measurement between access point and smartphone,” 11th International Conference on Knowledge, Information and Creativity Support Systems (KICSS), Yogyakarta, Indonesia, pp.1-6, 2016, doi: 10.1109/KICSS.2016.7951423.
 H. Robert, G. Schroth, S. Hilsenbeck, F. Schweiger and E. Steinbach, “Virtual reference view generation for CBIR-based visual pose estimation,” Proceedings of the 20th ACM international conference on Multimedia, Nara, Japan, pp.993-996, 2012.
 L. Wen, D. Wei, Q. Lai, X. Li and H. Yuan, “Geomagnetism-Aided Indoor Wi-Fi Radio-Map Construction via Smartphone Crowdsourcing,” Sensors, Vol.18, No.5, p.1462, May 2018, doi: 10.3390/s18051462.
 F. Martin A and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM Vol.24, No.6, pp.381-395, June 1981, doi: 10.1145/358669.358692.
 H. Yingming, F. Zhu, J. Ou, Q. Wu, J. Zhou and S. Fu, “Robust analysis of P3P pose estimation,” 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO), Sanya, China, pp.222-226, 2007.
 L. Vincent, F. Moreno-Noguer, P. Fua, “Epnp: An accurate o (n) solution to the pnp problem,” International journal of computer vision Vol.81, no.2, pp.155-166, February 2009, doi: 10.1007/s11263-008- 0152-6.
 S. Li, C. Xu, and M. Xie, “A robust O(n) solution to the perspective-n-point problem,” IEEE TPAMI, Vol.34, No.7, pp.1444–1450, July 2012, doi: 10.1109/TPAMI.2012.41.
 R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge Univ. Press, 2nd edition, 2003.
 S. Han, Y. Lee, J. Yun, C. Han, D. Lee and D. Suh, “Perspective Projection Algorithm Enabling Mobile Device’s Indoor Positioning,” Journal of Computer and Communications, Vol.6, No.1, p.159, December 2017, doi: 10.4236/jcc.2018.61017.