Skip to main content
Log in

Indoor Localization Methods Using Dead Reckoning and 3D Map Matching

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

In order to navigate or localize in 3D space such as parking garages, we would need height information in addition to 2D position. Conventionally, an altimeter is used to get the floor level/height information. We propose a novel method for three-dimensional navigation and localization of a land vehicle in a multi-storey parking-garage. The solution presented in this paper uses low cost gyro and odometer sensors, combined with a 3D map by means of particle filtering and collision detection techniques to localize the vehicle in a parking garage. This eliminates the necessity of an altimeter or other additional aiding sources such as radio signalling. Altimeters have inherent dynamic influential factors such as temperature and environmental pressure affecting the altitude readings, and for radio signals we need extra infrastructure requirements. The proposed solution can be used without any such additional infrastructure devices. Other sources of information, such as WLAN signals, can be used to complement the solution if and when available. In addition we extend this proposed method to novel concept of non-stationary 3D maps, as moving maps, within which localization of a track-able object is required. We also introduce novel techniques that enable seamless navigation solution from vehicular dead reckoning (VDR) to pedestrian dead reckoning (PDR) and vice versa to reduce user involvement. For achieving this we collect relevant measurements such as vehicle ignition status and accelerometer signal variance, and user pattern recognition to select appropriate dead reckoning method.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11

Similar content being viewed by others

References

  1. Chausse, F., Laneurit, J., and Chapuis, R. (2005). Vehicle localization on a digital map using particles filtering. In Proc. IEEE Intelligent Vehicles Symposium, Las Vegas, NV, 6-8 June (pp. 243-248).

  2. Liu, J., Chen, R., Chen, Y., Pei, L., & Chen, L. (2012). iParking: An Intelligent Indoor Location-Based Smartphone Parking Service. Sensors, 12(1), 14612–14629.

    Article  Google Scholar 

  3. Ascher, C., Kessler, C., Wankerl, M., & Trommer, G. G. (2010). Dual IMU Indoor Navigation with particle filter based map-matching on a smartphone. In Proc. International Conference on Indoor Positioning and Indoor Navigation, Zurich, Switzerland, 15-17 September.

  4. Parviainen, J., Kantola, J., Collin, J. (2008). Differential barometry in personal navigation. Position, Location and Navigation Symposium, IEEE/ION 5-8 May. doi: 10.1109/PLANS.2008.4570051.

  5. Bojja, J., Kirkko-jaakkola, M., Collin, J., & Takala, J. (2013). Indoor 3d navigation and positioning of vehicles in multi-storey parking garages. In Proc. ICASSP 2013, Vancouver, Canada, 26–31 May (pp. 2548–2552).

  6. French, R., & Lang, G. (1973). Automatic route control system. IEEE Transactions on Vehicular Technology, 22(2), 36–41.

    Article  Google Scholar 

  7. Gustafsson, F., Orguner, U., Schön, T. B., Skoglar, P., & Karlsson, R. (2012). Navigation and tracking of road-bound vehicles using map support. In A. Eskandarian (Ed.), Handbook of Intelligent Vehicles (pp. 397–434). London: Springer.

    Chapter  Google Scholar 

  8. Wagner, J., Isert, C., Purschwitz, A., & Kistner, A. (2010). Improved vehicle positioning for indoor navigation in parking garages through commercially available maps. In Proc. International Conference on Indoor Positioning and Indoor Navigation, Zurich, Switzerland, 15-17 September.

  9. Fouque, C., & Bonnifait, P. (2010). Multi-hypothesis map-matching on 3D navigable maps using raw GPS measurements. In Proc. IEEE Conference on Intelligent Transportation Systems (ITSC), 2010 13th International, 19-22 Sept (pp.1498, 1503).

  10. Pinto, M., Paulo Moreira, A., Matos, A., Sobreira, H., & Santos F. (2013). Fast 3D Map Matching Localisation Algorithm. Journal of Automation and Control Engineering, June. doi: 10.12720/joace.1.2.110-114.

  11. Yang, N., Tian, W. F., Jin, Z. H., & Zhang, C. B. (2005). Particle filter for sensor fusion in a land vehicle navigation system. Measurement Science and Technology, 16(3), 677.

    Article  Google Scholar 

  12. Fairfield, N., Wettergreen, D., & Kantor, G. (2010). Segmented SLAM in three-dimensional environments. Journal of Field Robotics, January, 27(1), 85–103.

    Article  Google Scholar 

  13. Kümmerle, R., Hähnel, D., Dolgov, D., Thrun, S., & Burgard, W. (2009). Autonomous driving in a multi-level parking structure. In Proc. IEEE International Conference on Robotics and Automation, Kobe, Japan, May (pp. 3395–3400).

  14. Leppäkoski, H., Collin, J., & Takala, J. (2012). Pedestrian navigation based on inertial sensors, indoor map, and WLAN signals. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto, Japan, 25-30 March (pp. 1569–1572).

  15. Kitanov, A.; Bisevac, S.; Petrovic, I. (2007). Mobile robot self-localization in complex indoor environments using monocular vision and 3D model. In Proc. International conference on Advanced intelligent mechatronics, IEEE/ASME, 4-7 Sept (pp.1,6).

  16. Gordon, N., Ristic, B., & Arulampalam. S. (2004). Beyond the Kalman filter: particle filters for tracking applications. Artech House radar library. Artech House, Boston, Mass. [u.a.].

  17. Chai, W., Chen, C., Edwan, E., Zhang, J., and Loffeld, O. (2012). INS/Wi-Fi based indoor navigation using adaptive Kalman filtering and vehicle constraints. In Proc. Workshop on Positioning Navigation and Communication, Dresden, Germany, 15-16 March (pp. 36–41).

  18. Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Radar and Signal Processing, IEEE Proceedings-F, 140(2), 107–113.

    Google Scholar 

  19. Zhu, L., Hyyppä, J., Ruizhi, C., & Zhengjun, L. (2010). An approach of 3D model simplification for mobile phone based navigation application. Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), 14-15 Oct (pp.1-4).

  20. Gerbaud, T., Polotski, V., & Cohen, P. (2004). Simultaneous exploration and 3D mapping of unstructured environments. In Proc. IEEE International Conference on Systems, Man and Cybernetics, 10-13 Oct (pp.5333–5337).

  21. Liu, T., Carlberg, M., Chen, G., Chen, J., Kua, J., & Zakhor, A. (2010). Indoor localization and visualization using a human-operated backpack system. In Proc. International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2010, 15-17 Sept (pp.1,10).

  22. Akenine-Möller, T., Haines, E., & Hoffman, N. (2008). Real-Time Rendering, third edition, A.K. Peters Ltd, Natick, MA (pp. 1045).

  23. Kantola, J., Perttunen, M., Leppänen, T., Collin, J., & Riekki, J. (2010). Context Awareness for GPS-Enabled Phones. In Proc. The 2010 International Technical Meeting of The Institute of Navigation, San Diego, CA, January (pp. 117–124).

  24. VTI SCC1300 MEMS gyro, product information. http://www.muratamems.fi/products/gyroscopes/scc1300-combined-gyroscope-and-accelerometer

  25. CarChip OBD II reader, product information. http://www.carchip.com/Products/8226.asp

  26. DGPS system - NovAtel DL-4 Plus, product information. http://www.novatel.com/assets/Documents/Papers/DL4plus.pdf

  27. Masakatsu. K, & Takeshi. K. (2003). Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera. In Proc. of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality. IEEE Computer Society.

  28. Chai; W., Chen; C., Edwan, E.; Zhang, J., & Loffeld, O. (2012). 2D/3D indoor navigation based on multi-sensor assisted pedestrian navigation in Wi-Fi environments. Ubiquitous Positioning, Indoor Navigation, and Location Based Service (UPINLBS), 3-4 Oct (pp.1,7).

  29. Freyberger, F., Kampmann, P., & Schmidt, G.K. (1990). Constructing maps for indoor navigation of a mobile robot by using an active 3D range imaging device. In Proc. IEEE International Workshop on Intelligent Robots and Systems . ‘Towards a New Frontier of Applications’, IROS’90, (pp. 143-148).

  30. Donate, A., & Xiuwen, L. (2010). 3D structure estimation from monocular video clips. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 13-18 June (pp.17–24).

  31. Dryanovski, I., Morris, W., & Jizhong, X. (2010). Multi-volume occupancy grids: An efficient probabilistic 3D mapping model for micro aerial vehicles. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 18-22 Oct (pp.1553–1559).

  32. Gutmann, J.-S., Fukuchi, M., & Fujita, M. (2005). A Floor and Obstacle Height Map for 3D Navigation of a Humanoid Robot. In Proc. IEEE International Conference on Robotics and Automation, 18-22 April (pp.1066–1071).

  33. Oliver, W., & Harle, R.. (2008). Pedestrian Localisation for Indoor Environments. In Proc. 10th International Conference on Ubiquitous Computing, 21-24 Sept (pp.114–123).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. Bojja.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bojja, J., Kirkko-Jaakkola, M., Collin, J. et al. Indoor Localization Methods Using Dead Reckoning and 3D Map Matching. J Sign Process Syst 76, 301–312 (2014). https://doi.org/10.1007/s11265-013-0865-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-013-0865-9

Keywords

Navigation