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Indoor localization system in a multi-block workspace

Published online by Cambridge University Press:  22 May 2009

JaeHyun Park ,
MunGyu Choi ,
YunFei Zu  and
JangMyung Lee
JaeHyun Park
Affiliation:
School of Electrical Engineering, Pusan National University, Pusan 609-735, Korea
MunGyu Choi
Affiliation:
School of Electrical Engineering, Pusan National University, Pusan 609-735, Korea
YunFei Zu
Affiliation:
School of Electrical Engineering, Pusan National University, Pusan 609-735, Korea
JangMyung Lee*
Affiliation:
School of Electrical Engineering, Pusan National University, Pusan 609-735, Korea
*
*Corresponding author. E-mail: jangmlee@hanmail.net
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Summary

This paper proposes methodologies and techniques for multi-block navigation of an indoor localization system with active beacon sensors. As service robots and ubiquitous technology have evolved, there is an increasing need for autonomous indoor navigation of mobile robots. In a large number of indoor localization schemes, the absolute position estimation method, relying on navigation beacons or landmarks, has been widely used due to its low cost and high accuracy. However, few of these schemes have managed to expand the applications for use in complicated workspaces involving many rooms or blocks that cover a wide region, such as airports and stations. Since the precise and safe navigation of mobile robots in complicated workspaces is vital for the ubiquitous technology, it is necessary to develop a multi-block navigation scheme. This new design of an indoor localization system includes ultrasonic attenuation compensation, dilution of both the precision analysis and fault detection, and an isolation algorithm using redundant measurements. These ideas are implemented on actual mobile robot platforms and beacon sensors, and experimental results are presented to test and demonstrate the new methods.

Keywords

Indoor localizationMulti-block navigationActive beaconMobile robot
Type
Article
Information
Robotica , Volume 28 , Issue 3 , May 2010 , pp. 397 - 403
Copyright
Copyright © Cambridge University Press 2009

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References

1.Randell, C. and Muller, H., “Low Cost Indoor Positioning System,” In: Ubiquitous Computing (Springer-Verlag, Sep. 2006) pp. 42–48.Google Scholar
2.Yi, S. Y. and Choi, B. W., “Autonomous navigation of indoor mobile robots using a global ultrasonic system,” Robotica 22, 369–374 (2004).Google Scholar
3.Tsai, C. C., “A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements,” Trans. Instrum. Meas. 47 (5), 1399–1404, (Oct. 1998).Google Scholar
4.Wang, J. P., Tian, W. F. and Jin, Z. H., “Study on integrated micro inertial navigation system/GPS for land vehicles,” Intell. Trans. Syst. 2, 1650–1653 (2003).Google Scholar
5.Davide, Merico and Bisiani, Roberto, “Indoor Navigation with Minimal Infrastructure,” Fourth Workshop on Position, Navigation and Communication, Hannover, Germany, 141–144, (2007).Google Scholar
6.Yun, J. M., Kim, S. B. and Lee, J. M., “Robust Positioning a Mobile Robot with Active Beacon Sensors,” LNAI 4251, ISSN 0302-9743, Part I, 890–897, (2006).Google Scholar
7.Kim, S. B. and Lee, J. M., “Precise indoor localization system for a mobile robot using auto calibration algorithm,” Korean Robot. Soc. 2 (1), 40–47 (2007).Google Scholar
8.Federico, Thomas and Ros, Lluís, “Revisiting trilateration for robot localization,” IEEE Trans. Robot. 21 (1), 93–101, (2005).Google Scholar
9.Manolakis, D. E., “Efficient solution and performance analysis of 3-D position estimation by trilateration,” IEEE Trans. Aerosp. Electron. Syst. 32, 1239–1248 (1996).Google Scholar
10.Grewal, M. S. and Andrews, A. P., Kalman Filtering: Theory and Practice Using MATLAB (Wiley, New York, 2000).Google Scholar
11.Golub, G. H. and Van Loan, C. F., Matrix Computations, (The Johns Hopkins University Press, Baltimore, MD, 1996).Google Scholar
12.Sturza, M. A., “Navigation system integrity monitoring using redundant measurements,” J. Inst. Navig. 35 (4), 483–501 (1988).Google Scholar
13.Tsai, Yi-Hsueh and Chang, Fan-Ren, “Using Multi-Frequency for GPS Positioning and Receiver Autonomous Integrity Monitoring,” Proceedings of the 2004 IEEE International Conference on Control Applications, Taipei, Taiwan, (2004).Google Scholar
14.Feng, S. and Ioannides, R. et al. , “A measurement domain receiver autonomous integrity monitoring algorithm,” GPS Solut. 10, 85–96 (2006).CrossRefGoogle Scholar