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Laser interferometry measurements based calibration and error propagation identification for pose estimation in mobile robots

Published online by Cambridge University Press:  06 August 2013

Paulo A. Jiménez  and
Bijan Shirinzadeh
Paulo A. Jiménez*
Affiliation:
Robotics and Mechatronics Research Laboratory (RMRL), Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria, Australia
Bijan Shirinzadeh
Affiliation:
Robotics and Mechatronics Research Laboratory (RMRL), Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria, Australia
*
*Corresponding author. E-mail: [email protected]
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Summary

A widely used method for pose estimation in mobile robots is odometry. Odometry allows the robot in real time to reconstruct its position and orientation from the wheels' encoder measurements. Given to its unbounded nature, odometry calculation accumulates errors with quadratic increase of error variance with traversed distance. This paper develops a novel method for odometry calibration and error propagation identification for mobile robots. The proposed method uses a laser-based interferometer to measure distance precisely. Two variants of the proposed calibration method are examined: the two-parameter model and the three-parameter model. Experimental results obtained using a Khepera 3 mobile robot showed that both methods significantly increase accuracy of the pose estimation, validating the effectiveness of the proposed calibration method.

Keywords

Mobile robotsNavigationRobot localisationPose estimation and registrationMechatronic systems
Type
Articles
Information
Robotica , Volume 32 , Issue 1 , January 2014 , pp. 165 - 174
Copyright
Copyright © Cambridge University Press 2013 

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References

1.Alici, G. and Shirinzadeh, B., “A systematic technique to estimate positioning errors for robot accuracy improvement using laser interferometry based sensing,” Mech. Mach. Theory 40 (8), 879–906 (Aug. 2005).CrossRefGoogle Scholar
2.Antonelli, G., Chiaverini, S. and Fusco, G., “A calibration method for odometry of mobile robots based on the least-squares technique: Theory and experimental validation,” IEEE Trans. Robot. 21 (5), 994–1004 (Oct. 2005).CrossRefGoogle Scholar
3.Borenstein, J. and Feng, L., “Umbmark: A Benchmark Test for Measuring Odometry Errors in Mobile Robots,” SPIE Conference on Mobile Robots, Philadelphia (22–26 Oct. 1995).Google Scholar
4.Goel, P., Roumeliotis, S. I. and Sukhatme, G. S., “Robust Localization Using Relative and Absolute Position Estimates,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, Kyongju, South Korea (17–21 Oct. 1999), pp. 1134–1140.Google Scholar
5.Lagarias, J. C., Reeds, J. A., Wright, M. H. and Wright, P. E., “Convergence properties of the Nelder–Mead simplex method in low dimensions,” SIAM J. Optim. 9 (1), 112–147 (1998).CrossRefGoogle Scholar
6.Martinelli, A. and Siegwart, R., “Estimating the Odometry Error of a Mobile Robot during Navigation,” European Conference on Mobile Robots, Warsaw, Poland (4–6 Sep. 2003).Google Scholar
7.Roy, N. and Thrun, S., “Online Self-Calibration for Mobile Robots,” In: IEEE International Conference on Robotics and Automation, vol. 3, Detroit, MI, USA (10–15 May 1999) pp. 2292–2297.Google Scholar
8.Smith, R. C. and Cheeseman, P., “On the representation and estimation of spatial uncertainty,” Int. J. Robot. Res. 5 (4), 56–68 (1986).CrossRefGoogle Scholar
9.Teoh, P. L., Shirinzadeh, B., Foong, C. W. and Alici, G., “The measurement uncertainties in the laser interferometry-based sensing and tracking technique,” Measurement 32 (2), 135–150 (2002).CrossRefGoogle Scholar