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Performance Improvement for Mobile Robot Position Determination Using Cubature Kalman Filter

Published online by Cambridge University Press:  02 October 2017

Jafar Zarei  and
Abdolrahman Ramezani
Jafar Zarei*
Affiliation:
(Department of Control Engineering, School of Electrical and Electronics Engineering, Shiraz University of Technology, Modarres BLVD. P.O. Box, 71555-313, Shiraz-IRAN)
Abdolrahman Ramezani
Affiliation:
(Department of Control Engineering, School of Electrical and Electronics Engineering, Shiraz University of Technology, Modarres BLVD. P.O. Box, 71555-313, Shiraz-IRAN)
*
(E-mail: zarei@sutech.ac.ir)
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Abstract

The objective of this paper is to accurately determine mobile robots' position and orientation by integrating information received from odometry and an inertial sensor. The position and orientation provided by odometry are subject to different types of errors. To improve the odometry, an inertial measurement unit is exploited to give more reliable attitude information. However, the nonlinear dynamic of these systems and their complexities such as different sources of errors make navigation difficult. Since the dynamic models of navigation systems are nonlinear in practice, in this study, a Cubature Kalman Filter (CKF) has been proposed to estimate and correct the errors of these systems. The information from odometry and a gyroscope are integrated using a CKF. Simulation results are provided to illustrate the superiority and the higher reliability of the proposed approach in comparison with conventional nonlinear filtering algorithms such as an Extended Kalman Filter (EKF).

Keywords

Mobile robotPosition determinationData integrationNonlinear filteringCubature Kalman Filter
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 2 , March 2018 , pp. 389 - 402
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Arasaratnam, I. and Haykin, S. (2009). Cubature Kalman filters. IEEE Transactions on Automatic Control, 54(6), 1254–1269.Google Scholar
Barshan, B. and Durrant-Whyte, H.F. (1995). Inertial navigation systems for mobile robots. IEEE Transactions on Robotics and Automation, 11(3), 328–342.Google Scholar
Borenstein, J. and Feng., L. (1995). UMBmark: A benchmark test for measuring odometry errors in mobile robots. Proceedings of the 1995 SPIE Conference on Mobile Robots, Philadelphia, USA.Google Scholar
Borenstein, J. and Feng, L. (1996a). Gyrodometry: A new method for combining data from gyros and odometry in mobile robots. Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, USA.Google Scholar
Borenstein, J. and Feng, L. (1996b). Measurement and correction of systematic odometry errors in mobile robots. IEEE Transactions on Robotics and Automation, 12(6), 869–880.Google Scholar
Canelon, J.I., Provence, R.S., Shieh, L.S. and Liu, C.R. (2007). A simple recursive method for stationary receiver position estimation using GPS difference measurements. ISA transactions, 46(2), 147–155.Google Scholar
Gadsden, S.A., Al-Shabi, M., Arasaratnam, I. and Habibi, S.R. (2014). Combined cubature Kalman and smooth variable structure filtering: A robust nonlinear estimation strategy. Signal Processing, 96, 290–299.Google Scholar
Ge, Q., Xu, D. and Wen, C. (2014). Cubature information filters with correlated noises and their applications in decentralized fusion. Signal Processing, 94, 434–444.Google Scholar
Grewal, M.S. and Andrews, A.P. (2011). Kalman filtering: theory and practice using MATLAB. John Wiley & Sons.Google Scholar
Gundimada, S. and Asari, V.K. (2009). Facial recognition using multisensor images based on localized kernel eigen spaces. IEEE Transactions on Image Processing, 18(6), 1314–1325.Google Scholar
Hanlin, Sheng and Zhang, T. (2015). MEMS-based low-cost strap-down AHRS research. Measurement, 59(1), 63–72.Google Scholar
Houshangi, N. and Azizi, F. (2005). Accurate mobile robot position determination using unscented Kalman filter. Proceedings of the Canadian Conference on Electrical and Computer Engineering, Saskatoon, Sask., Canada.Google Scholar
Hu, H. and Huang, X. (2010). SINS/CNS/GPS integrated navigation algorithm based on UKF. Journal of Systems Engineering and Electronics, 21(1), 102–109.Google Scholar
Jia, B., Xin, M. and Cheng, Y. (2013). High-degree cubature Kalman filter. Automatica, 49(2), 510–518.Google Scholar
Jwo, D.-J., Hu, C.-W. and Tseng, C.-H. (2013). Nonlinear Filtering with IMM Algorithm for Ultra-Tight GPS/INS Integration. International Journal of Advanced Robotic Systems, 10(5), 222–236.Google Scholar
Lü, S., Xie, L. and Chen, J. (2009). New techniques for initial alignment of strapdown inertial navigation system. Journal of the Franklin Institute, 346(10), 1021–1037.Google Scholar
Sadeghzadeh-Nokhodberiz, N., Poshtan, J., Wagner, A., Nordheimer, E. and Badreddin, E. (2014a). Cascaded Kalman and particle filters for photogrammetry based gyroscope drift and robot attitude estimation. ISA transactions, 53(2), 524–532.Google Scholar
Sadeghzadeh-Nokhodberiz, N., Poshtan, J., Wagner, A., Nordheimer, E. and Badreddin, E. (2014b). Distributed observers for pose estimation in the presence of inertial sensory soft faults. ISA transactions, 53(4), 1307–1319.CrossRefGoogle ScholarPubMed
Schleicher, D., Bergasa, L.M., Ocaña, M., Barea, R. and López, E. (2010). Low-cost GPS sensor improvement using stereovision fusion. Signal Processing, 90(12), 3294–3300.Google Scholar
Sun, Q., Diao, M., Zhang, Y. and Li, Y. (2017). Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation. Symmetry, 9, 1–15.Google Scholar
Teslić, L., Škrjanc, I. and Klančar, G. (2010). Using a LRF sensor in the Kalman-filtering-based localization of a mobile robot. ISA transactions, 49(1), 145–153.Google Scholar
Titterton, D. and Weston, J.L. (2004). Strapdown inertial navigation technology. IET.Google Scholar
Wendel, J., Meister, O., Schlaile, C. and Trommer, G.F. (2006). An integrated GPS/MEMS-IMU navigation system for an autonomous helicopter. Aerospace Science and Technology, 10(6), 527–533.Google Scholar
Xu, B., Gao, W., Liu, Y.L. and Liu, Y. (2014). Dual-model reverse CKF algorithm in cooperative navigation for USV. Mathematical Problems in Engineering, 2014, Article ID 186785.Google Scholar
Xue, L., Jiang, C.-Y., Chang, H.-L., Yang, Y., Qin, W. and Yuan, W.-Z. (2012). A novel Kalman filter for combining outputs of MEMS gyroscope array. Measurement, 45(4), 745–754.Google Scholar
Zarei, J. and Shokri, E. (2015). Convergence analysis of non-linear filtering based on cubature Kalman filter. IET Science, Measurement & Technology, 9(3), 294–305.Google Scholar
Zhao, Y. and Slotine, J.-J.E. (2005). Discrete nonlinear observers for inertial navigation. Systems & control letters, 54(9), 887–898.Google Scholar