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Using Constraints for Shoe Mounted Indoor Pedestrian Navigation

Published online by Cambridge University Press:  25 November 2011

Khairi Abdulrahim ,
Chris Hide ,
Terry Moore  and
Chris Hill
Khairi Abdulrahim
Affiliation:
Nottingham Geospatial Institute (NGI), Nottingham, UK. Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM), Malaysia.
Chris Hide
Affiliation:
Nottingham Geospatial Institute (NGI), Nottingham, UK.
Terry Moore*
Affiliation:
Nottingham Geospatial Institute (NGI), Nottingham, UK.
Chris Hill
Affiliation:
Nottingham Geospatial Institute (NGI), Nottingham, UK.
*
(E-mail: isxka3@nottingham.ac.uk)
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Abstract

Shoe mounted Inertial Measurement Units (IMU) are often used for indoor pedestrian navigation systems. The presence of a zero velocity condition during the stance phase enables Zero Velocity Updates (ZUPT) to be applied regularly every time the user takes a step. Most of the velocity and attitude errors can be estimated using ZUPTs. However, good heading estimation for such a system remains a challenge. This is due to the poor observability of heading error for a low cost Micro-Electro-Mechanical (MEMS) IMU, even with the use of ZUPTs in a Kalman filter. In this paper, the same approach is adopted where a MEMS IMU is mounted on a shoe, but with additional constraints applied. The three constraints proposed herein are used to generate measurement updates for a Kalman filter, known as ‘Heading Update’, ‘Zero Integrated Heading Rate Update’ and ‘Height Update’.

The first constraint involves restricting heading drift in a typical building where the user is walking. Due to the fact that typical buildings are rectangular in shape, an assumption is made that most walking in this environment is constrained to only follow one of the four main headings of the building. A second constraint is further used to restrict heading drift during a non-walking situation. This is carried out because the first constraint cannot be applied when the user is stationary. Finally, the third constraint is applied to limit the error growth in height. An assumption is made that the height changes in indoor buildings are only caused when the user walks up and down a staircase. Several trials were shown to demonstrate the effectiveness of integrating these constraints for indoor pedestrian navigation. The results show that an average return position error of 4·62 meters is obtained for an average distance of 1557 meters using only a low cost MEMS IMU.

Keywords

Inertial Measurement Units (IMU)Zero Integrated Heading Rate (ZIHR)Zero Integrated Heading Rate (ZIHR)
Type
Research Article
Information
The Journal of Navigation , Volume 65 , Issue 1 , January 2012 , pp. 15 - 28
Copyright
Copyright © The Royal Institute of Navigation 2011

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References

REFERENCES

Abdulrahim, K., Hide, C., Moore, T. and Hill, C. (2010). Aiding MEMS IMU with building heading for indoor pedestrian navigation. Proceedings of Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), 1–6. Helsinki, Finland.Google Scholar
Abdulrahim, K., Hide, C., Moore, T. and Hill, C. (2011). Aiding Low Cost Inertial Navigation with Building Heading for Pedestrian Navigation. The Journal of Navigation, 64, 219–233.CrossRefGoogle Scholar
Foxlin, E. (2005). Pedestrian Tracking with Shoe-Mounted Inertial Sensors. Computer Graphics and Applications, IEEE. 25, 38–46.CrossRefGoogle ScholarPubMed
Godha, S. and Lachapelle, G. (2008). Foot Mounted Inertial System for Pedestrian Navigation. Measurement Science and Technology, 19, 075202.CrossRefGoogle Scholar
Godha, S. (2006). Performance Evaluation of Low Cost MEMS-based IMU Integrated with GPS for Land Vehicle Navigation Application. MSc Thesis, Department of Geomatics Engineering. Alberta: Canada.Google Scholar
Hide, C., Botterill, T. and Andreotti, M. (2009). An Integrated IMU, GNSS and Image Recognition Sensor for Pedestrian Navigation. Proceedings of International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2009), 527–537, Savannah, USA.Google Scholar
Jimenez, F. S. A. R., Prieto, C. and Guevara, J. (2009). A Comparison of Pedestrian Dead-Reckoning Algorithms using a Low-Cost MEMS IMU. Proceedings of IEEE International symposium on Intelligent Signal Processing Budapest, Hungary.CrossRefGoogle Scholar
Lachapelle, G. (2004). GNSS Indoor Location Technologies. The International Symposium on GNSS/GPS, Sydney, Australia.CrossRefGoogle Scholar
Odijk, D. and Kleijer, F. (2007). Performance of high-sensitivity GPS for personal navigation at Schiphol Airport, the Netherlands. International Global Navigation Satellite Systems Society (IGNSS) Symposium, Sydney, Australia.Google Scholar
Odijk, D. and Kleijer, F. (2008). Can GPS Be Used for Location Based Services at Schiphol Airport, the Netherlands?, Proceedings of the 5th Workshop on Positioning, Navigation and Communications 2008 (WPNC 08), 143–148, Hannover, Germany.CrossRefGoogle Scholar
Pang, G. and Liu, H. (2001). Evaluation of a low-cost MEMS accelerometer for distance measurement. Journal of Intelligent and Robotic Systems, 30, 249–265.CrossRefGoogle Scholar
Park, M. and Gao, Y. (2006). Error analysis and stochastic modeling of low-cost MEMS accelerometer. Journal of Intelligent and Robotic Systems, 46, 27–41.CrossRefGoogle Scholar
Shin, E. H. (2005). Estimation Techniques for Low Cost Inertial Navigation. PhD Thesis, Department of Geomatics Engineering. Calgary: University of Calgary, 206.Google Scholar
Skog, I., Nilsson, J. O. and Händel, P. (2010). Evaluation of Zero-Velocity Detectors for Foot-Mounted Inertial Navigation Systems, Proceedings of International Conference on Indoor Positioning and Indoor Navigation (IPIN 2010), Switzerland.CrossRefGoogle Scholar