Skip to main content

Indoor Positioning and Navigation Methods Based on Mobile Phone Camera

  • Conference paper
  • First Online:
Multimedia Technology and Enhanced Learning (ICMTEL 2021)

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Brito, J.H., Angst, R., Köser, K., et al.: Radial Distortion Self-Calibration, Computer Vision and Pattern Recognition, pp. 1368–1375. IEEE, June 2013

    Google Scholar 

  2. Bukhari, F., Dailey, M.N.: Automatic radial distortion estimation from a single image. J. Math. Imaging Vis. 45(1), 1–45 (2013)

    Article  MathSciNet  Google Scholar 

  3. 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)

    Google Scholar 

  4. Fiore, P.D.: Efficient linear solution of exterior orientation. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 140–148 (2011)

    Article  Google Scholar 

  5. 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)

    Google Scholar 

  6. Schweighofer, G., Pinz, A.: Robust pose estimation from a planar target. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2024–2030 (2006)

    Article  Google Scholar 

  7. Strobl,K.H., Hirzinger,G.: More accurate pinhole camera calibration with imperfect planar target. IEEE Int. Conf. Comput. Vis. Workshops 1068–1075 (2011)

    Google Scholar 

  8. Ma, S.D., Zhang, Z.Y.: Computer Vision-The Theory of Computer and Basis of Algorithm. Science Press, China (1997)

    Google Scholar 

  9. Kuthirummal, S., Jawahar, C.V., Narayanan, P.J.: Planar shape recognition across multiple views. Int. Conf. Patt. Recogn. 1, 456–459 (2002)

    Article  Google Scholar 

  10. Kukelova, Z., Pajdla, T.: A minimal solution to radial distortion autocalibration. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2410–2422 (2011)

    Article  Google Scholar 

  11. 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)

    Google Scholar 

  12. Kanatani, K., Ohta, N., Kanazawa, Y.: Optimal homography computation with a reliability measure. In: IAPR workshop on Machine Vision Applications, pp. 426–429 (1998)

    Google Scholar 

  13. Liu, R., Ruan, Z.C., Wei, S.: Algorithm research on monochronous matrix in plane measurement. J. Syst. Simul. 13(suppl.), 174–176 (2011)

    Google Scholar 

  14. Wang, Y.X., Ma, Y., Chen, Q.X.: A method of line matching based on feature points. J. Softw. 7(7), 1539–1545 (2012)

    Google Scholar 

  15. Xu, D., Tan, M., Li, Y.: Robot Vision Measurement and Control. National Defense Industry Press, China (2008)

    Google Scholar 

  16. Xu, D., Tan, M., Li, Y.: Visual Measurement and Control for Robots. National Defense Industry Press, China, pp. 35–39 (2011)

    Google Scholar 

  17. Liu, R., Wei, S.: Research on plane Measurement method based on Image. M.S. thesis, Elect. Inf. Eng., University of Anhui, Anhui, China (2002)

    Google Scholar 

  18. Han, Y.X., Zhang, Z.C., Dai, M.: Monocular vision measurement method for target ranging. Opt. Precis. Eng. 19(5), 1110–1117 (2011)

    Article  Google Scholar 

  19. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision: Camera Models. Cambridge University Press, vol. 30 (9–10), pp. 1865–1872 (2004)

    Google Scholar 

  20. Zhang, Y., Liu, Y.: Closed-from solution for circle pose estimation using binocular stereo vision. Electron. Lett. 44(21), 1246–1247 (2008)

    Article  Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Google Scholar 

  23. 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)

    Google Scholar 

  24. Li, H.X., Wen, X., Guo, H., et al.: Research into Kinect/Inertial Measurement Units Based on Indoor Robots. Sensors 18(3), 839 (2018)

    Article  Google Scholar 

  25. Chen, X.L.: Research of attitude calculation of single camera visual system. Chin. J. Sci. Instrument 35(S1), 45–48 (2014)

    Google Scholar 

  26. 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)

    Google Scholar 

  27. 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)

    Google Scholar 

  28. 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)

    Google Scholar 

  29. 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)

    Google Scholar 

  30. 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)

    Article  Google Scholar 

  31. Guo, H., Li, H., Xiong, J., Yu, M.: Indoor positioning system based on particle swarm optimization algorithm. Measurement 134, 908–913 (2019)

    Article  Google Scholar 

  32. 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)

    Google Scholar 

  33. 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

    Google Scholar 

  34. 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)

    Article  Google Scholar 

  35. 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)

    Article  Google Scholar 

  36. Xu, Y., Tian, G., Chen, X.: Enhancing INS/UWB integrated position estimation using federated EFIR filtering. IEEE Access 6, 64461–64469 (2018)

    Article  Google Scholar 

  37. 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)

    Article  Google Scholar 

  38. 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)

    Article  Google Scholar 

  39. 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)

    Article  Google Scholar 

  40. 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)

    Google Scholar 

  41. 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

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hang Guo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-82562-1_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82561-4

  • Online ISBN: 978-3-030-82562-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics