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MEMS IMU Based Pedestrian Indoor Navigation for Smart Glass

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Abstract

In open air environment, Global Positioning System (GPS) receiver can determine its position with very high accuracy. Inside a building the GPS signal is degraded, the position estimation from the GPS receiver is very erroneous and of no practical use. In this paper, we present an indoor navigation system to track the position of a pedestrian by using built-in inertial measurement unit (IMU) sensors of a smart eyeglass. The device used for this project was an intelligent eye-wear “JINS MEME”. Here algorithm for step detection, heading and stride estimation are used to estimate the position based on the known locations of the walker using Pedestrian Dead Reckoning method (PDR). We have used extended Kalman filters as sensor fusion algorithm, where measurements of acceleration and orientation from IMU are used to track user’s movement, pace, and heading. The results showed that the level of accuracy was entirely acceptable. Average deviancy between the estimated and real position was less than 1.5 m for short range of walk was accomplished. There are some ideas for further development. Increasing the accuracy of the position estimation by palliation of stride length estimation error was identified as the most essential.

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References

  1. Mezentsev, O., & Lachapelle, G. (2005). Pedestrian dead reckoning–A solution to navigation in GPS signal degraded areas. Geomatica, 59(2), 175–182.

    Google Scholar 

  2. Chakraborty, A., Priyantha, N. B., & Balakrishnan, H. (2000). The cricketlocation-support system. In Advance computing and machinery (ACM) mobile computer and networking (pp. 32–43).

  3. Mezrich, O., Kollnescher, T., & Fainguelernt, J. (2010). RSS based localization in a wireless sensor network. In 4th European education and research conference (EDERC 2010) (pp. 255–258).

  4. Beauregard, S. (2006) A helmet-mounted pedestrian dead reckoning system. In 3rd international forum on applied wearable computing (pp. 1–11).

  5. Kappi, A., Syrjarinne, J., & Saarinen, J. (2001). Mems-imu based pedestrian navigator for handheld devices. Proceedings of the 14th international technical meeting of the satellite division of the institute of navigation (ION GPS 2001) (pp. 1369–1373).

  6. Abyarjo, F., Barreto, A., Cofino, J., & Ortega, F. (2012). Implementing a sensor fusion algorithm for 3d orientation detection with inertial/magnetic sensors. Mobile System, 12, 197.

    Google Scholar 

  7. Cornman, A., & Mei, D. (2016). Extended Kalman filtering, course report. Stanford: Stanford University.

    Google Scholar 

  8. Thong, Y. K., Woolfson, M. S., Crowe, J. A., Hayes-Gill, B. R., & Challis, R. E. (2002). Dependence of inertial measurements of distance on accelerometer noise. Measurement Science and Technology, 13(8), 1163.

    Article  Google Scholar 

  9. Godha, S., & Lachapelle, G. (2008). Foot mounted inertial system for pedestrian navigation. Measurement Science and Technology, 19(7), 75–82.

    Article  Google Scholar 

  10. Zhao, H., Wang, Z., Gao, Q., Hassan, M. M., & Alelaiwi, A. (2015). Smooth estimation of human foot motion for the zero-velocity-update-aided inertial pedestrian navigation system. Sensor Review, 35, 389–400.

    Article  Google Scholar 

  11. Wang, Z., Zhao, H., Qiu, S., & Gao, Q. (2015). Stance-phase detection for zupt-aided foot-mounted pedestrian navigation system. IEEE/ASME Transactions on Mechatronics, 20, 3170–3181.

    Article  Google Scholar 

  12. Ojeda, L., & Borenstein, J. (2007). Personal dead-reckoning system for GPS-denied environments. IEEE International workshop on safety, security and rescue robotics (pp. 1–6).

  13. Ren, M., Pan, K., Liu, Y., Guo, H., Zhang, X., & Wang, P. (2016). A novel pedestrian navigation algorithm for a foot-mounted inertial-sensor-based system. MDPI sensors, 16, 139.

    Article  Google Scholar 

  14. Kim, J. W., Jang, H. J., Hwang, D.-H., & Park, C. (2004). A step, stride and heading determination for the pedestrian navigation system. Journal in Global Positioning Systems, 3(8), 273–279.

    Article  Google Scholar 

  15. Norrdine, A., Kasmi, Z., & Blankenbach, J. (2016). Step detection for sustained inertial pedestrian navigation system using a foot-mounted permanent magnet. IEEE Sensors Journal, 16, 6766–6773.

    Article  Google Scholar 

  16. Park, S. K., & Suh, Y. S. (2010). A zero velocity detection algorithm using inertial sensors for pedestrian navigation systems. Sensors, 10(10), 9163–9178.

    Article  Google Scholar 

  17. Fang, L., Antsaklis, P. J., Montestruque, L. A., McMickell, M. B., Lemmon, M., Sun, Y., et al. (2005). Design of a wireless assisted pedestrian dead reckoning system—The navigate experience. IEEE Transactions on Instrumentation and Measurement, 54, 2342–2358.

    Article  Google Scholar 

  18. Bonilla, M. N. I., Escamilla-Ambrosio, P. J., & Cortés, J. M. R. (2011). Pedestrian dead reckoning towards indoor location-based applications. In 8th International Conference on Electrical Engineering, Computing Science and Automatic Control (pp. 1–6).

  19. Jin, Y., Toh, H. S., Soh, W. S., & Wong, W. C. (2011). A robust dead-reckoning pedestrian tracking system with low-cost sensors. IEEE International Conference on Pervasive Computing and Communications (PerCom) (pp 222–230).

  20. Collin, J., Mezentsev, O., & Lachapelle, G. (2003). Indoor positioning system using accelerometry and high accuracy heading sensors. In GPS/GNSS (Vol. C3)

  21. Evennou, F., & Marx, F. (2006). Advanced integration of wifi and inertial navigation systems for indoor mobile positioning. EURASIP Journal on Advances in Signal Processing, 2006(1), 086706.

    Article  Google Scholar 

  22. Dippold, M. (2006). Personal dead reckoning with accelerometers. 3rd International Forum on Applied Wearable Computing, 2006, 1–6.

    Google Scholar 

  23. Ladetto, Q., & Merminod, B. (2002). In step with ins: Navigation for the blind, tracking emergency crews. GPS World, 13(10), 30.

    Google Scholar 

  24. Judd, T. (1997). A personal dead reckoning module. In Proceeding of the Institute of Navigation

  25. Frank, K., Krach, B., Catterall, N., & Robertson, P. (2009). Development and evaluation of a combined WLAN and inertial indoor pedestrian positioning system. In Proceedings of the 22nd international technical meeting of the satellite division of the institute of navigation (ION GNSS 2009) (pp. 538–546).

  26. Murata, Y., Kaji, K., Hiroi, K., & Kawaguchi, N. (2014). Pedestrian dead reckoning based on human activity sensing knowledge. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing: Adjunct Publication, UbiComp ’14 Adjunct, (New York, NY, USA) (pp. 797–806). ACM

  27. Cho, S. Y., Lee, K. W., Park, C. G., & Lee, J. G. (2003). A personal navigation system using low-cost MEMS/GPS/Fluxgate. In Proceedings of the 59th Annual Meeting of the Institute of navigation and CIGTF 22nd Guidance Test Symposium, Albuquerque, NM, USA.

  28. Yamauchi, K., Bhanu, B., & Saito, H. (2009). Recognition of walking humans in 3D: Initial results. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops (CVPR Workshops) (pp. 45–52).

  29. Kokshenev, V. B. (2004). Dynamics of human walking at steady speeds. Phys. Rev. Lett., 93, 208–212.

    Article  Google Scholar 

  30. Li, F., Zhao, C., Ding, G., Gong, J., Liu, C., & Zhao, F. (2012). A reliable and accurate indoor localization method using phone inertial sensors. In Proceedings of the 2012 ACM conference on ubiquitous computing, pp. 421–430.

  31. Lin, T., Li, L., & Lachapelle, G. (2015). Multiple sensors integration for pedestrian indoor navigation. In International conference on indoor positioning and indoor navigation (IPIN), 13–16 October 2015, Banff, Alberta, Canada.

  32. Wetzstein, G. (2016). Advanced IMU sensor fusion with Kalman filtering. Stanford: Stanford University.

    Google Scholar 

  33. Brown, R. G., & Hwang, P. Y. C. (2008). Introduction to random signals and applied Kalman filtering (4th ed.). New York: Wiley.

    MATH  Google Scholar 

  34. Welch, G., & Bishop, G. (2001). An introduction to Kalman filter. Tech. Rep., Los Angels, CA.

  35. Bleser, D. G., & Stricker, P. (2012). Using the Kalman and extended Kalman filter. Tech. Rep., German Research Center for Artificial Intelligence

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Authors and Affiliations

  1. Department of EEE, City University-Bangladesh, Dhaka, Bangladesh

    Md Abid Hasan

  2. School of Engineering and Computer Science, BRAC University, Dhaka, Bangladesh

    Mohammad Nasimuzzaman Mishuk

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  1. Md Abid Hasan
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  2. Mohammad Nasimuzzaman Mishuk
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Correspondence to Md Abid Hasan.

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Hasan, M.A., Mishuk, M.N. MEMS IMU Based Pedestrian Indoor Navigation for Smart Glass. Wireless Pers Commun 101, 287–303 (2018). https://doi.org/10.1007/s11277-018-5688-3

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  • DOI: https://doi.org/10.1007/s11277-018-5688-3

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