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Motion monitoring based on a finite state machine for precise indoor localization

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Abstract

This paper presents a precise stance detection method for accurate personal localization using a foot-mounted inertial measurement unit. The exact classification of the stance phases of the foot is realized with a finite state machine (FSM), which separates the human gait circle in different sub-states. The FSM-based approach provides high accurate and robust detections of Zero Velocity Updates (ZUPTs) which can be applied to the navigation filter. We use a constraint stochastic cloning (SC) Kalman filter to show the performance of the high precise ZUPT intervals with real world sensor data including forward, backward and staircase motion. Even for the movement type running and the signals of an ultra-low cost inertial measurement unit we achieve with our motion monitoring system a position estimation with an average error of less than 1.5% of the travelled distance.

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References

  1. Ruppelt, J., Kronenwett, N. and Trommer, G. F., High-precision and robust indoor localization based on foot-mounted inertial sensors, Position Location and Navigation Symposium (PLANS), 2016, pp. 67–75.

    Google Scholar 

  2. Ruppelt, J., Kronenwett, N. and Trommer, G. F., A novel finite state machine based step detection technique for pedestrian navigation systems, Indoor Positioning and Indoor Navigation (IPIN), 2015.

    Google Scholar 

  3. Ruppelt, J., Merz, P., and Trommer, G.F., IndoorGuide–A multi sensor pedestrian navigation system for precise and robust indoor localization, Inertial Sensors and Systems (ISS), 2016.

    Google Scholar 

  4. Foxlin, E., Pedestrian tracking with shoe-mounted inertial sensors, IEEE Comput. Grap. Appl, 2005, vol. 25, no. 6, pp. 38–46.

    Article  Google Scholar 

  5. Cho, S.Y. and Park, C.G., MEMS based pedestrian navigation system, The Journal of Navigation, 2006, vol. 59, no. 01, pp. 135–153.

    Article  Google Scholar 

  6. Godha, S. and Lachapelle, G., Foot mounted inertial system for pedestrian navigation, Meas. Sci. Technol, 2008, vol. 19, no. 7, pp. 1–9.

    Article  Google Scholar 

  7. Ascher, C., Modulares multisensorielles Indoor Navigationssystem, Berlin: Logos Berlin, 2014.

    Google Scholar 

  8. Hide, C., Botterill, T. and Andreotti, M., An integrated IMU, GNSS and image recognition sensor for pedestrian navigation, Proceedings of the 22nd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2009), 2009, pp. 527–537.

    Google Scholar 

  9. Callmer, J., Törnqvist, D. and Gustafsson, F., Probabilistic stand still detection using foot mounted IMU, 13th Conference on Information Fusion (FUSION), 2010.

    Google Scholar 

  10. Skog, I., Nilsson, J. and Händel, P., Evaluation of zerovelocity detectors for foot-mounted inertial navigation systems, Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2010.

    Google Scholar 

  11. Lee, J., Shin, B., Lee, S., Park, J., Kim, J., Kim, C. and Lee, T., A step length estimation based on motion recognition and adaptive gait cognition using a smartphone, Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014), 2014, pp. 243–249.

    Google Scholar 

  12. Roumeliotis, S.I. and Burdick, J.W., Stochastic cloning: A generalized framework for processing relative state measurements, Proceedings of IEEE International Conference on Robotics and Automation (ICRA '02), 2002, vol. 2, pp. 1788–1795.

    Google Scholar 

  13. Perry, J. and Burnfield, J., Gait analysis: Normal and pathological function, Thorofare: Slack, 2010, 2nd ed.

    Google Scholar 

  14. Groves, P., Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, GNSS technology and applications series, Artech House, 2008.

    Google Scholar 

  15. Abdulrahim, K., Hide, C., Moore, T. and Hill, C., Using constraints for shoe mounted indoor pedestrian navigation, The Journal of Navigation, 2012, vol. 65, no. 01, pp. 15–28.

    Article  Google Scholar 

  16. Abdulrahim, K., Hide, C., Moore, T. and Hill, C., Aiding MEMS IMU with building heading for indoor pedestrian navigation, in 2010 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPIN-LBS), pp. 1–6.

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Authors and Affiliations

  1. Institute of Systems Optimization (ITE), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    N. Kronenwett, J. Ruppelt & G. F. Trommer

  2. ITMO University, St. Petersburg, Russia

    G. F. Trommer

Authors
  1. N. Kronenwett
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  2. J. Ruppelt
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  3. G. F. Trommer
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Corresponding author

Correspondence to N. Kronenwett.

Additional information

Published in Russian in Giroskopiya i Navigatsiya, 2017, No. 1, pp. 33–48.

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Kronenwett, N., Ruppelt, J. & Trommer, G.F. Motion monitoring based on a finite state machine for precise indoor localization. Gyroscopy Navig. 8, 190–199 (2017). https://doi.org/10.1134/S2075108717030063

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  • DOI: https://doi.org/10.1134/S2075108717030063

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