A Technique to Estimate the Mobile Position and Rotation

Authors

  • Trinh Hien Anh Researcher, Institute of Information Technology, VAST
  • Trinh Xuan Hung Researcher, Institute of Information Technology, VAST
  • Ha Manh Toan Researcher, Institute of Information Technology, VAST

DOI:

https://doi.org/10.24297/ijct.v22i.9335

Keywords:

AR, VR, Camera pose, Marker detect

Abstract

Nowadays, almost all applications, especially augmented reality (AR) applications, run on the web. In these applications, determining the position and rotation of the mobile device is an indispensable basic step. The speed and accuracy of this work greatly affect the quality of the user experience. Therefore, estimating the rotational position of mobile devices on the Web is necessary and meaningful in practice. In this paper, we propose a way to estimate the position and rotation of the mobile device using the image of the marker obtained from the camera combined with the data from the device’s tilt angle sensor. The proposal has been experimentally installed and evaluated for performance on
Web Assembly, Java Script, and C++ platforms. Along with that, to ensure objectivity, we compared the speed and calculation error of the proposed technique with the P3P and PnP techniques installed in the OpenCV open-source library. We also use the proposal method to develop the Virtual Museum application for the Vietnam National Museum of Nature

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References

Bay, H., Tuytelaars, T., & Van Gool, L. “Surf: Speeded up robust features”. In European conference on computer vision. Springer, Berlin, Heidelberg, pp. 404-417, 2006.

Camposeco, F., Cohen, A., Pollefeys, M., Sattler, “T. Hybrid camera pose estimation”. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 136-144, 2018.

Damera-Venkata, Niranjan, and Brian L. Evans. "Adaptive threshold modulation for error diffusion halftoning." IEEE Transactions on Image Processing 10.1 (2001): 104-116.

Debye, P., "Näherungsformeln für die Zylinderfunktionen für große Werte des Arguments und unbeschränkt veränderliche Werte des Index", Mathematische Annalen, 67(4), pp. 535–558, 1909.

Gratton, Serge, Amos S. Lawless, and Nancy K. Nichols. "Approximate Gauss–Newton methods for nonlinear least squares problems". SIAM Journal on Optimization 18.1, pp.106-132, 2007

Lowe, D. G. “Distinctive image features from scale-invariant keypoints”. International journal of computer vision, 60(2), pp.91-110, 2004.

M. Luessi, M. Eichmann, G. M. Schuster, and A. K. Katsaggelos, Framework for efficient optimal multilevel image thresholding, Journal of Electronic Imaging, vol. 18, pp. 013004+, 2009. doi:10.1117/1.3073891.

Romero-Ramire, F. J., Muñoz-Salinas, R., & Medina-Carnicer, R.. “Fractal Markers: a new approach for long-range marker pose estimation under occlusion”. IEEE Access, 7, pp.169908-169919, 2019.

Sauvola, J., & Pietikäinen, M. (2000) Adaptive document image binarization. Pattern Recognition, 33(2), 225–236. doi:10.1016/s0031-3203(99)00055-2

Y.K. Lai, P.L. Rosin, Efficient Circular Thresholding, IEEE Trans. on Image Processing 23(3), pp. 992–1001 2014). doi:10.1109/TIP.2013.2297014.

W3C Candidate Recommendation Draft, 24 August 2022 Accessing virtual reality (VR) and augmented reality (AR) devices, including sensors and head-mounted displays, on the Web. [Online] . Available: https://www.w3.org/TR/webxr.

W3C Working Draft, 2 September 2021 Base orientation sensor interface and concrete sensor subclasses to monitor the device’s physical orientation in relation to a stationary three dimensional Cartesian coordinate system. [Online]. Available https://www.w3.org/TR/orientation-sensor.

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Published

2022-12-29

How to Cite

Anh, . . T. H., Hung, T. X., & Toan , H. M. . (2022). A Technique to Estimate the Mobile Position and Rotation. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 22, 171–180. https://doi.org/10.24297/ijct.v22i.9335

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Research Articles