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Aiding Low Cost Inertial Navigation with Building Heading for Pedestrian Navigation

Published online by Cambridge University Press:  02 March 2011

Khairi Abdulrahim ,
Chris Hide ,
Terry Moore  and
Chris Hill
Khairi Abdulrahim*
Affiliation:
(Institute of Engineering Surveying and Space Geodesy (IESSG), University of Nottingham)
Chris Hide
Affiliation:
(Institute of Engineering Surveying and Space Geodesy (IESSG), University of Nottingham)
Terry Moore
Affiliation:
(Institute of Engineering Surveying and Space Geodesy (IESSG), University of Nottingham)
Chris Hill
Affiliation:
(Institute of Engineering Surveying and Space Geodesy (IESSG), University of Nottingham)
*
(Email: [email protected])
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Abstract

In environments where GNSS is unavailable or not useful for positioning, the use of low cost MEMS-based inertial sensors has paved a way to a more cost effective solution. Of particular interest is a foot mounted pedestrian navigation system, where zero velocity updates (ZUPT) are used with the standard strapdown navigation algorithm in a Kalman filter to restrict the error growth of the low cost inertial sensors. However heading drift still remains despite using ZUPT measurements since the heading error is unobservable. External sensors such as magnetometers are normally used to mitigate this problem, but the reliability of such an approach is questionable because of the existence of magnetic disturbances that are often very difficult to predict. Hence there is a need to eliminate the heading drift problem for such a low cost system without relying on external sensors to give a possible stand-alone low cost inertial navigation system. In this paper, a novel and effective algorithm for generating heading measurements from basic knowledge of the orientation of the building in which the pedestrian is walking is proposed to overcome this problem. The effectiveness of this approach is demonstrated through three field trials using only a forward Kalman filter that can work in real-time without any external sensors. This resulted in position accuracy better than 5 m during a 40 minutes walk, about 0·1% in position error of the total distance. Due to its simplistic algorithm, this simple yet very effective solution is appealing for a promising future autonomous low cost inertial navigation system.

Keywords

MEMSINSPedestrian Navigation.
Type
Research Article
Information
The Journal of Navigation , Volume 64 , Issue 2 , April 2011 , pp. 219 - 233
Copyright
Copyright © The Royal Institute of Navigation 2011

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References

REFERENCES

Beauregard, S. (2007). Omnidirectional Pedestrian Navigation for First Responders. Proceedings of 4th Workshop on Positioning, Navigation and Communication (WPNC 07), Hannover, 33–36.Google Scholar
Butler, D. (2006). Virtual globes: The web-wide world. Nature 439(7078): 776–778.CrossRefGoogle ScholarPubMed
Collin, J., Mezentsev, O., Lachappelle, G. (2003). Indoor Positioning System Using Accelerometry and High Accuracy Heading Sensors. Proceedings of GPS/GNSS 2003, Portland.Google Scholar
El-Sheimy, N., Shin, E.-H. s., Jiniu, X., (2006). Kalman Filter face off: Extended vs Unscented Kalman filters for Integrated GPS and MEMS Inertial. Inside GNSS, Spirent Communications. 1: 7.Google Scholar
Feliz, R., Zalama, E., Gomez, J., (2009). Pedestrian tracking using inertial sensors. Journal of Physical Agents 3(1): 35.Google Scholar
Foxlin, E. (2005). Pedestrian Tracking with Shoe-Mounted Inertial Sensors. IEEE Computer Graphics and Applications: 38–46.CrossRefGoogle ScholarPubMed
Godha, S. and Lachapelle, G. (2008). Foot mounted inertial system for pedestrian navigation. Measurement Science and Technology 19(7): 075202.CrossRefGoogle Scholar
Grejner-Brzezinska, D. A., Yi, Y., Toth, C. K., (2001). Bridging GPS gaps in urban canyons: The benefits of ZUPTs. NAVIGATION (Washington, DC) 48(4), 217–225.Google Scholar
Hide, C., Botterill, T., Andreotti, M. (2009). An Integrated IMU, GNSS and Image Recognition Sensor for Pedestrian Navigation. Proceedings of ION GNSS 2009. USA.Google Scholar
Hide, C. and Moore, T. (2005). GPS and Low Cost INS Integration for Positioning in the Urban Environment. Proceedings of the Institute of Navigation GNSS 2005.Google Scholar
Hide, C., Moore, T., Hill, C., (2007). A Multi-Sensor Navigation Filter for High Accuracy Positioning in all Environments. The Journal of Navigation 60, 409–425.CrossRefGoogle Scholar
Hide, C., Pinchin, J., and Park, D. (2007). Development of a low cost multiple GPS antenna attitude system. Proceedings of the Institute of Navigation, GNSS 2007.Google Scholar
Jadaliha, M., Shahri, A. M., Mobed, M. (2008). A new Pedestrian Navigation System based on a Low-Cost IMU. The 5th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI 2008).Google Scholar
Rajagopal, S. (2008). Personal Dead Reckoning System with Shoe Mounted Inertial Sensors. Master's thesis, Royal Institute of Technology, Stockholm.Google Scholar
Robertson, P., Angermann, M., Krach, B. (2009). Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors. Proceedings of 11th ACM International Conference on Ubiquitous Computing (Ubicomp 2009), Orlando, 93–96.Google Scholar
Simon, D. (2001). Kalman filtering. Embedded Systems Programming14(6): 72–79.Google Scholar
Stirling, R., Collin, J., Fyfe, K., Lachapelle, G. (2003). An Innovative Shoe-Mounted Pedestrian Navigation System. Proceeding of European Navigation Conference 2003.Google Scholar
Titterton, D. H. and Weston, J. L. (2004). Strapdown Inertial Navigation Technology, IET.CrossRefGoogle Scholar