Gyroscopy and Navigation

, Volume 6, Issue 3, pp 188–196 | Cite as

Navigation algorithm combining building plans with autonomous sensor data

  • P. Davidson
  • M. Kirkko-Jaakkola
  • J. Collin
  • J. Takala


This paper presents an approach to navigation system’s position and heading correction using building floor plans. The algorithm includes three steps: (a) autonomous sensors data processing to obtain position and heading, (b) map-matching correction, and (c) navigation system errors estimation. A particle filter is used to incorporate the building plan information and a Kalman filter estimates the dead reckoning error states. This algorithm was designed for vehicle navigation systems operating inside buildings with known floor plans and can be adapted for implementation on real-time navigation systems using low-cost MEMS gyroscope and speed sensor as dead reckoning instruments. The real-world data collected from the vehicle indoor tests has shown that the proposed algorithm is able to correct significant errors in dead reckoning position and heading by applying the map constraints.


Global Navigation Satellite System Kalman Filter Global Navigation Satellite System Navigation System Particle Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Davidson, P., Collin, J., and Takala, J., Map-aided autonomous pedestrian navigation system, 18th International Conference on Integrated Navigation Systems, St. Petersburg, Russia, May 2011, pp. 314–318.Google Scholar
  2. 2.
    Davidson, P., Collin, J., and Takala, J., Application of particle filters for indoor positioning using floor plans, in Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), IEEE, 2010, pp. 1–4.Google Scholar
  3. 3.
    Davidson, P., Collin, J., and Takala, J., Application of particle filters to a map-matching algorithm, Gyroscopy and Navigation, 2011, vol. 2, no. 4, pp. 285–292.CrossRefGoogle Scholar
  4. 4.
    Abdulrahim, K., Hide, C., Moore, T., and Hill, C., Integrating low cost IMU with building heading in indoor pedestrian navigation, J. Global Positioning Systems, 2011, vol. 10, no. 1, pp. 30–38.CrossRefGoogle Scholar
  5. 5.
    Abdulrahim, K., Hide, C., Moore, T., and Hill, C., Aiding low cost inertial navigation with building heading for pedestrian navigation, Journal of Navigation, 2011, vol. 64, no. 2, pp. 219–233.CrossRefGoogle Scholar
  6. 6.
    Borenstein, J., Ojeda, L., and Kwanmuang, S., Heuristic reduction of gyro drift for personnel tracking systems, Journal of Navigation, 2009, vol. 62, no. 01, pp. 41–58.CrossRefGoogle Scholar
  7. 7.
    Borenstein, J. and Ojeda, L., Heuristic drift elimination for personnel tracking systems, Journal of Navigation, 2010, vol. 63, no. 4, pp. 591–606.CrossRefGoogle Scholar
  8. 8.
    Jiménez, A., Seco, F., Zampella, F., Prieto, J., and Guevara, J., Improved heuristic drift elimination (iHDE) for pedestrian navigation in complex buildings, in Proc. Int. Conf. Indoor Positioning and Indoor Navigation, Guimaraes, Portugal. IEEE, Sep. 2011.Google Scholar
  9. 9.
    Gilliéron, P., Buchel, D., Spassov, I., and Merminod, B., Indoor navigation performance analysis, Proc. of the European Navigation Conference GNSS, 2004.Google Scholar
  10. 10.
    Spassov, I., Algorithms for map-aided autonomous indoor pedestrian positioning and navigation, Ph.D. dissertation, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, 2007.Google Scholar
  11. 11.
    Dmitriev, S., Stepanov, A., Rivkin, B., and Koshaev, D., Optimal map-matching for car navigation systems, 6th International Conference on Integrated Navigation Systems, St. Petersburg, Russia, May 1999.Google Scholar
  12. 12.
    Davidson, P., Collin, J., Raquet, J., and Takala, J., Application of particle filters for vehicle positioning using road maps, Proc. 23 rd ION GNSS, Portland, OR, Sep. 2010, pp. 1653–1661.Google Scholar
  13. 13.
    Klepal, M., Beauregard, S. et al., A backtracking particle filter for fusing building plans with PDR displacement estimates, Proc. 5 th Workshop on Positioning, Navigation and Communication, WPNC’08, Hannover, Germany. IEEE, Mar. 2008, pp. 207–212.Google Scholar
  14. 14.
    Beauregard, S., Omnidirectional pedestrian navigation for first responders, Proc. 4th Workshop on Positioning, Navigation and Communication, WPNC’07, Hannover, Germany. IEEE, Mar. 2007, pp. 33–36.CrossRefGoogle Scholar
  15. 15.
    Woodman, O. and Harle, R., Pedestrian localisation for indoor environments, Proc. of UbiComp 08, September 21-24, 2008, Seoul, Korea.Google Scholar
  16. 16.
    Ascher, C., Kessler, C., Weis, R., and Trommer, G., Multi-floor map matching in indoor environments for mobile platforms, Proc. of Int. Conf. on Indoor Positioning and Indoor Navigation, Nov 13-15, 2012, Sydney, Australia, 2012.Google Scholar
  17. 17.
    Krach, B. and Robertson, P., Integration of footmounted inertial sensors into a bayesian location estimation framework, Proc. 5 th Workshop on Positioning, Navigation and Communication, WPNC’08, Hannover, Germany. IEEE, Mar. 2008, pp. 55–61.CrossRefGoogle Scholar
  18. 18.
    Khider, M., Kaiser, S., Robertson, P., and Angermann, M., The effect of maps-enhanced novel movement models on pedestrian navigation performance, Proc. 12 th European Navigation Conference, Apr. 22-28, Toulouse, France, 2008.Google Scholar
  19. 19.
    Kaiser, S., Khider, M., and Robertson, P., A human motion model based on maps for navigation systems, EURASIP Journal on Wireless Communications and Networking, 2011, no. 2011:60.Google Scholar
  20. 20.
    Pinchin, J., Hide, C., and Moore, T., A particle filter approach to indoor navigation using a foot mounted inertial navigation system and heuristic heading information, Proc. Indoor Positioning and Indoor Navigation (IPIN), 2012 Int. Conf. on. IEEE, 2012, pp. 1–10.CrossRefGoogle Scholar
  21. 21.
    Dellaert, F., Fox, D., Burgard, W., and Thrun, S., Monte Carlo localization for mobile robots, IEEE International Conference on Robotics and Automation (ICRA99), May 1999.Google Scholar
  22. 22.
    Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., and Nordlund, P.-J., Particle filters for positioning, navigation, and tracking, Signal Processing, IEEE Transactions on, 2002, vol. 50, no. 2, pp. 425–437.CrossRefGoogle Scholar
  23. 23.
    Thrun, S., Burgard, W., and Fox, D., Probabilistic Robotics, MIT Press, 2005.zbMATHGoogle Scholar
  24. 24.
    Zhou, H. and Sakane, S., Sensor planning for mobile robot localization—a hierarchical approach using a bayesian network and a particle filter, Robotics, IEEE Transactions on, 2008, vol. 24, no. 2, pp. 481–487.Google Scholar
  25. 25.
    Maaref, H. and Barret, C., Sensor-based navigation of a mobile robot in an indoor environment, Robotics and Autonomous systems, 2002, vol. 38, no. 1, pp. 1–18.CrossRefzbMATHGoogle Scholar
  26. 26.
    Zhuang, Y., Wang, K., Wang, W., and Hu, H., A hybrid sensing approach to mobile robot localization in complex indoor environments, Int. J. of Robotics and Automation, 2012, vol. 27, no. 2, p. 198.CrossRefGoogle Scholar
  27. 27.
    Perälä, T. and Ali-Löytty, S., Kalman-type positioning filters with floor plan information, Proc. 6th Int. Conf. Advances in Mobile Computing & Multimedia, Nov. 2008, pp. 350–355.CrossRefGoogle Scholar
  28. 28.
    Gordon, N.J., Salmond, D.J., and Smith, A.F., Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEEE Proc. Radar Signal Process., 1993, vol. 140, no. 2, pp. 107–113.CrossRefGoogle Scholar
  29. 29.
    Ristic, B., Arulampalam, S. and Gordon, N., Beyond the Kalman filter: Particle filters for tracking applications, Artech House Publishers, 2004.Google Scholar
  30. 30.
    Kitagawa, G., Monte Carlo filter and smoother for non-Gaussian nonlinear state space models, J. Comput. Graph. Statist., 1996, vol. 5, no. 1, pp. 1–25.MathSciNetGoogle Scholar
  31. 31.
    Liu J.S. and Chen, R., Sequential Monte Carlo methods for dynamic systems, J. Am. Statist. Assoc., 1998, vol. 93, no. 443, pp. 1032–1044.CrossRefzbMATHGoogle Scholar
  32. 32.
    Pekkalin, O., Leppäkoski, H., Hautamäki, J., Collin, J., and Takala, J., Reference for indoor location systems using gyroscope and quadrature incremental encoder, Proc. 23rd ION GNSS, Portland, OR, Sep. 2010, pp. 1192–1197.Google Scholar
  33. 33.
    Murata Electronics. SCR1100 Gyroscopes,, 2014, [Online; accessed]Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2015

Authors and Affiliations

  • P. Davidson
    • 1
  • M. Kirkko-Jaakkola
    • 2
  • J. Collin
    • 3
  • J. Takala
    • 3
  1. 1.Department of Information and Navigation SystemsITMO UniversitySt. PetersburgRussia
  2. 2.Finnish Geospatial Research InstituteNational Land SurveyKirkkonummiFinland
  3. 3.Department of Pervasive ComputingTampere University of TechnologyKirkkonummiFinland

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