Abstract
The vision technology has been used for the indoor positioning based on a mobile phone camera. In this paper, we studied the 2D positioning method by analyzing the single frame image, obtaining the camera’s interior/exterior orientation parameters through the image calibration procedure, and calculating the coordinates with the homography matrix. Further, the mobile phone camera has been used for the indoor navigation. The image data is processed and converted into the mobile phone’s moving distance and the attitude by the coordinate transformation method (a four-parameter fitting model), and the trajectory of the mobile phone can be calculated by the visual navigation method. In the first experiment, four points have been selected as the calibration points, and the positioning method has been conducted and analyzed. The experimental results with the software GIANT show that the error is 0.192 m in the area 9.6 m × 3.2 m, which reached a high accuracy of the indoor positioning. In the second experiment, a mobile phone has been moved inside the lab room, image data was collected, the trajectory was calculated with the navigation method proposed, and the mean error of 0.685 m has been obtained. Both results explained that the proposed methods can effectively improve the accuracy and stability of indoor positioning and navigation.
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References
Brito, J.H., Angst, R., Köser, K., et al.: Radial Distortion Self-Calibration, Computer Vision and Pattern Recognition, pp. 1368–1375. IEEE, June 2013
Bukhari, F., Dailey, M.N.: Automatic radial distortion estimation from a single image. J. Math. Imaging Vis. 45(1), 1–45 (2013)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)
Fiore, P.D.: Efficient linear solution of exterior orientation. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 140–148 (2011)
Shi, B., Matsushita, Y., Wei, Y., Xu, C., Tan, P.: Self-calibrating photometric stereo. In: IEEE Conference on computer vision and pattern recognition (CVPR) - San Francisco, CA, USA (2010.06.13-2010.06.18), pp. 1118–1125 (2010)
Schweighofer, G., Pinz, A.: Robust pose estimation from a planar target. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2024–2030 (2006)
Strobl,K.H., Hirzinger,G.: More accurate pinhole camera calibration with imperfect planar target. IEEE Int. Conf. Comput. Vis. Workshops 1068–1075 (2011)
Ma, S.D., Zhang, Z.Y.: Computer Vision-The Theory of Computer and Basis of Algorithm. Science Press, China (1997)
Kuthirummal, S., Jawahar, C.V., Narayanan, P.J.: Planar shape recognition across multiple views. Int. Conf. Patt. Recogn. 1, 456–459 (2002)
Kukelova, Z., Pajdla, T.: A minimal solution to radial distortion autocalibration. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2410–2422 (2011)
Jain, P.K., Jawahar, C.V.: Homography estimation from planar contours. In: The Third International Symposium on 3D Date Processing Visualization and Transmission, pp. 877–884 (2006)
Kanatani, K., Ohta, N., Kanazawa, Y.: Optimal homography computation with a reliability measure. In: IAPR workshop on Machine Vision Applications, pp. 426–429 (1998)
Liu, R., Ruan, Z.C., Wei, S.: Algorithm research on monochronous matrix in plane measurement. J. Syst. Simul. 13(suppl.), 174–176 (2011)
Wang, Y.X., Ma, Y., Chen, Q.X.: A method of line matching based on feature points. J. Softw. 7(7), 1539–1545 (2012)
Xu, D., Tan, M., Li, Y.: Robot Vision Measurement and Control. National Defense Industry Press, China (2008)
Xu, D., Tan, M., Li, Y.: Visual Measurement and Control for Robots. National Defense Industry Press, China, pp. 35–39 (2011)
Liu, R., Wei, S.: Research on plane Measurement method based on Image. M.S. thesis, Elect. Inf. Eng., University of Anhui, Anhui, China (2002)
Han, Y.X., Zhang, Z.C., Dai, M.: Monocular vision measurement method for target ranging. Opt. Precis. Eng. 19(5), 1110–1117 (2011)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision: Camera Models. Cambridge University Press, vol. 30 (9–10), pp. 1865–1872 (2004)
Zhang, Y., Liu, Y.: Closed-from solution for circle pose estimation using binocular stereo vision. Electron. Lett. 44(21), 1246–1247 (2008)
Zhang, Q.D., Fan, J.S.: Application and development of satellite navigation and positioning technology in China. J. Navig. Positioning 4(3), 82–88 (2016)
Dwiyasa, F., Lim, M.H.: A survey of problems and approaches in wireless-based indoor positioning. In: International Conference on Indoor Positioning and Indoor Navigation, pp. 1–7. IEEE (2016)
Di, K.C., Wan, W.H., Zhao, H.Y., et al.: Progress and applications of visual SLAM. Acta Geodaetica et Cartographica Sinica 47(6), 770–779 (2018)
Li, H.X., Wen, X., Guo, H., et al.: Research into Kinect/Inertial Measurement Units Based on Indoor Robots. Sensors 18(3), 839 (2018)
Chen, X.L.: Research of attitude calculation of single camera visual system. Chin. J. Sci. Instrument 35(S1), 45–48 (2014)
Feng, K.Q., Li, J., Zhang, X.M., et al.: A new quaternion-based Kalman filter for real-time attitude estimation using the two-step geometrically-intuitive correction algorithm. Sensors 17(9), 2146 (2017)
Song, H.H., Yu, G.X., Qu, Y.B.: Monitoring and forecasting system for ship attitude motion based on extended Kalman filtering algorithm. J. Chin. Inertial Technol. 26(1), 6–12 (2018)
Li, J., et al.: High-precision attitude measurement algorithm based on complementary filtering and Kalman filtering. J. Chin. Inertial Technol. 26(1), 51–55+86 (2018)
Mu, X.F., Chen, J., Zhou, Z.X., et al.: accurate initial state estimation in a monocular visual – inertial SLAM system. Sensor 18(2), 506 (2018)
Feng, G., Huang, X.: Observability analysis of navigation system using point-based visual and inertial sensors. Optik – Int. J. Light Electron Opt. 125(3), 1346–1353 (2014)
Guo, H., Li, H., Xiong, J., Yu, M.: Indoor positioning system based on particle swarm optimization algorithm. Measurement 134, 908–913 (2019)
Guo, H., Tian, B.L., Yu, M., Deng, L.K., Wang, H.T.: Improved ambiguity searching method of ultra-short baseline with nonlinear constraint. In: Proceedings of the 2018 International Technical Meeting of the Institute of Navigation, Reston, Virginia, pp. 46–55 (2018)
Guo, H., Uradzinski, M.: The usability of MTI IMU sensor data in PDR indoor positioning. In: 2018 25th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS 2018), May 2018
Xu, Y., Ahn, C.K., Shmaliy, Y.S., et al.: Adaptive robust INS/UWB-integrated human tracking using UFIR filter bank. Measurement 123, 1–7 (2018)
Xu, Y., Shmaliy, Y.S., Li, Y., Chen, X., Guo, H.: Indoor ins/lidar-based robot localization with improved robustness using cascaded fir filter. IEEE Access 7(1), 34189–34197 (2019)
Xu, Y., Tian, G., Chen, X.: Enhancing INS/UWB integrated position estimation using federated EFIR filtering. IEEE Access 6, 64461–64469 (2018)
Xu, Y., Karimi, H.R., Li, Y.Y., Zhou, F.Y., Bu, L.L.: Real-time accurate pedestrian tracking using EFIR filter bank for tightly coupling recent inertial navigation system and ultra-wideband measurements. Proc. Inst. Mech. Eng. Part I-J. Syst. Control Eng. 232(4), 464–472 (2018)
Xu, Y., Chen, X.: Online cubature Kalman filter Rauch–Tung–Striebel smoothing for indoor inertial navigation system/ultrawideband integrated pedestrian navigation. Proc. Inst. Mech. Eng. Part I-J. Syst. Control Eng. 232(4), 390–398 (2018)
Xu, Y., Shmaliy, Y.S., Li, Y., Chen, X.: UWB-based indoor human localization with time-delayed data using EFIR filtering. IEEE Access 5(1), 16676–16683 (2017)
Uradzinski, M., Guo, H., Mugnier, C.: Checking the accuracy of an inertial-based pedestrian navigation system with a drone. GPS World 28(6), 58–64 (2017)
Uradzinski, M., Guo, H., Liu, X., Yu, M.: Advanced indoor positioning using Zigbee wireless technology. Wireless Pers. Commun. 97(4), 6509–6518 (2017). https://doi.org/10.1007/s11277-017-4852-5
Acknowledgments
The paper was supported by the projects of the National Key R&D Program of China (No. 2016YFB0502204), National Natural Science Foundation of China (No. 41764002), and the corresponding author is Prof. Hang Guo, hguo@ncu.edu.cn.
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Yu, M., Yu, J., Li, H., Li, H., Guo, H. (2021). Indoor Positioning and Navigation Methods Based on Mobile Phone Camera. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-82562-1_39
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