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Single and Double Reference Points Based High Precision 3D Indoor Positioning with Camera and Orientation-Sensor on Smart Phone

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Abstract

This paper proposes a high precision 3D indoor positioning method, which depends on one or two reference points in scene and camera and orientation-sensor on smart phone. The proposed method has the merits of implementing simplicity, high accuracy, friendly availability and low cost. The proposed method adopts only single or double reference points and camera imaging model for camera calibrating and 3D positioning. Classical camera calibration method uses a set of reference points, but the proposed camera calibration method is easier to implement. Traditional computer vision based positioning method, P3P, utilizes at least three points and has multiple solutions. The proposed positioning method is equal to P2P or P1P, but is faster and has unique solution. The proposed method achieves the high 3D positioning precision at the level less than 30 cm. The proposed method employs smart phone as hardware and software carrier, which is more usable than other methods. In a word, the proposed method is very suitable for low-cost and high precision 3D indoor positioning.

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References

  1. Harle, R. (2013). A survey of indoor inertial positioning systems for pedestrians. IEEE Communications Surveys and Tutorials, 15(3), 1281–1293.

    Article  Google Scholar 

  2. Al Nuaimi, K. (2011). A survey of indoor positioning systems and algorithms, In Proceedings of international conference on innovations in information technology (pp. 185–190).

  3. Mautz, R. (2011). Survey of optical indoor positioning systems, In Proceedings of international conference on indoor positioning and indoor navigation (pp. 1–7).

  4. Atia, M. M. (2013). Dynamic online-calibrated radio maps for indoor positioning in wireless local area networks. IEEE Transactions on Mobile Computing, 12(9), 1774–1787.

    Article  MATH  Google Scholar 

  5. Kim, J. (2008). Vision-based location positioning using augmented reality for indoor navigation. IEEE Transactions on Consumer Electronics, 54(3), 954–962.

    Article  Google Scholar 

  6. Lee, S. (2006). A pyroelectric infrared sensor-based indoor location-aware system for the smart home. IEEE Transactions on Consumer Electronics, 52(4), 1311–1317.

    Article  MATH  Google Scholar 

  7. Suski, W. (2013). Using a map of measurement noise to improve UWB indoor position tracking. IEEE Transactions on Instrumentation and Measurement, 62(8), 2228–2236.

    Article  Google Scholar 

  8. Wang, T. Q. (2013). Position accuracy of time-of-arrival based ranging using visible light with application in indoor localization systems. Journal of Lightwave Technology, 31(20), 3302–3308.

    Article  MATH  Google Scholar 

  9. Gritton, C.W.K. (2012). MEMS sensors: Enabling next-gen content discovery, navigation and interactivity in CE devices, In Proceedings of IEEE international symposium on consumer electronics (pp. 1–8).

  10. Atzori, L. (2012) Indoor navigation system using image and sensor data processing on a smartphone, In Proceedings of international conference on optimization of electrical and electronic equipment (pp. 1158–1163).

  11. Wang, Q. (2012). Accurate indoor tracking using a mobile phone and non-overlapping camera sensor networks,” In Proceedings of IEEE international instrumentation and measurement technology conference (pp. 2022–2027).

  12. Taylor, B. (2012). Smart phone-based Indoor guidance system for the visually impaired, In Proceedings of international conference on control automation robotics and vision (pp. 871–876).

  13. Ruotsalainen, L. (2011). Heading change detection for indoor navigation with a smartphone camera, In Proceedings of international conference on indoor positioning and indoor navigation (pp. 1–7).

  14. Werner, M. (2011). Indoor positioning using smartphone camera, In Proceedings of international conference on indoor positioning and indoor navigation (pp. 1–6).

  15. Juang, S.Y. (2012). Real-time indoor surveillance based on smartphone and mobile robot, In Proceedings of IEEE international conference on industrial informatics (pp. 475–480).

  16. Ascher, C. (2010). Dual IMU Indoor Navigation with particle filter based map-matching on a smartphone, In Proceedings of international conference on indoor positioning and indoor navigation, (pp. 1–5).

  17. Van Vinh, N. (2012). Self-positioning system for indoor navigation on mobile phones, In Proceedings of IEEE international conference on consumer electronics (pp. 114–115).

  18. Zhang, R. (2013). Indoor localization using a smart phone, In Proceedings of IEEE sensors applications symposium (pp. 38–42).

  19. Faragher, R.M. (2012). Opportunistic radio SLAM for indoor navigation using smartphone sensors, In Proceedings of IEEE/ION position location and navigation symposium (pp. 120–128).

  20. Khalifa, S. (2013). Converting context to indoor position using built-in smartphone sensors, In Proceedings of IEEE international conference on pervasive computing and communications workshops (pp. 423–424).

  21. Kang, W. (2012). Improved heading estimation for smartphone-based indoor positioning systems, In Proceedings of IEEE international symposium on personal indoor and mobile radio communications (pp. 2449–2453).

  22. Pratama, A.R. (2012) Smartphone-based Pedestrian Dead Reckoning as an indoor positioning system, In Proceedings of international conference on system engineering and technology (pp. 1–6).

  23. Lukianto, C. (2010). Pedestrian smartphone-based indoor navigation using ultra-portable sensory equipment, In Proceedings of international conference on indoor positioning and indoor navigation (pp. 1–5).

  24. Ausmeier, B. (2012). Indoor navigation using a mobile phone, In Proceedings of african conference on software engineering and applied computing (pp. 109–115).

  25. Haralick, R. M. (1994). Review and analysis of solutions of the three point perspective pose estimation problem. International Journal of Computer Vision, 13(3), 331–356.

    Article  Google Scholar 

  26. Gao, X. (2003). Complete solution classification for the perspective-three-point problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(8), 930–943.

    Article  Google Scholar 

  27. Ahmadinejad, F. (2013). A low-cost vision-based tracking system for position control of quadrotor robots, In Proceedings of RSI/ISM international conference on robotics and mechatronics (pp. 356–361).

  28. Dementhon, D. F. (1995). Model-based object pose in 25 lines of code. International Journal of Computer Vision, 15(1), 123–141.

    Article  Google Scholar 

  29. Qingxuan, J. (2006). The study of positioning with high-precision by single camera based on P3P algorithm, In Proceedings of IEEE international conference on industrial informatics (pp. 1385–1388).

  30. D’Alfonso, L. (2013). P3P and P2P problems with known camera and object vertical directions, In Proceedings of Mediterranean conference on control and automation, (pp. 444–451).

  31. Aanæs, H. (2012). Interesting Interest Points. International Journal of Computer Vision, 97(1), 18–35.

    Article  Google Scholar 

  32. Gauglitz, S. (2011). Evaluation of interest point detectors and feature descriptors for visual tracking. International Journal of Computer Vision, 94(3), 335–360.

    Article  Google Scholar 

  33. Keller, F. (2012). Calibration of smartphones for the use in indoor navigation, In Proceedings of international conference on indoor positioning and indoor navigation (pp. 1–8).

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Authors and Affiliations

  1. Physics College of Science and Technology, Yangzhou University, Yangzhou, 225000, Jiangsu, China

    Honggui Li

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  1. Honggui Li
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Correspondence to Honggui Li.

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Li, H. Single and Double Reference Points Based High Precision 3D Indoor Positioning with Camera and Orientation-Sensor on Smart Phone. Wireless Pers Commun 83, 1995–2011 (2015). https://doi.org/10.1007/s11277-015-2499-7

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