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Robust Square-Root Cubature FastSLAM with Genetic Operators

Published online by Cambridge University Press:  26 August 2020

Ramazan Havangi Open the ORCID record for Ramazan Havangi [Opens in a new window]
Ramazan Havangi*
Affiliation:
Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
*
*Corresponding author. E-mail: [email protected]
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Summary

An improved FastSLAM based on the robust square-root cubature Kalman filter (RSRCKF) with partial genetic resampling is proposed in this paper. In the proposed method, RSRCKF is used to design the proposal distribution of FastSLAM and to estimate environment landmarks. The proposed method does not require a priori knowledge of the noise statistics. In addition, to increase diversity, it uses the genetic operators-based strategy to further improve the particle diversity. In fact, a partial genetic resampling operation is carried out to maintain the diversity of particles. The proposed method is compared with other methods via simulation and experimental data. It can be seen from the results that the proposed method provides significantly more accurate and robust estimation results compared with other methods even with fewer particles and unknown a priori. In addition, the consistency of the proposed method is better than that of other methods.

Keywords

SLAMH-infinity filteringParticle filterCubature Kalman filterUFastSLAM
Type
Articles
Information
Robotica , Volume 39 , Issue 4 , April 2021 , pp. 665 - 685
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Durrant-Whyte, H. and Bailey, T., “Simultaneous localization and mapping: Part I the essential algorithms,” IEEE Robot. Automat. Magaz. 13(2), 99–110 (2006).Google Scholar
Huang, S. and Dissanayake, G., “Convergence and consistency analysis for extended Kalman filter based SLAM,” IEEE Trans. Robot. 23(5), 1036–1049 (2007).Google Scholar
Kurt Yavuz, Z. and Yavuz, S., “A Comparison of EKF, UKF, FastSLAM2.0, and UKF-based FastSLAM Algorithms,” IEEE 16th International Conference on Intelligent Engineering Systems, Lisbon, Portugal (2012).Google Scholar
Thrun, S., Montemerlo, M., Koller, D., Wegbreit, B., Nieto, J. and Nebot, E., “FastSLAM: An efficient solution to the simultaneous localization and mapping problem with unknown data association,” J. Mach. Learn. Res. 4(3), 380–407 (2004).Google Scholar
Liu, D., Duan, J. and Shi, H., “A strong tracking square root central difference FastSLAM for unmanned intelligent vehicle with adaptive partial systematic resampling,” IEEE Trans. Intell. Transp. Syst. 17(1), 3110–3120 (2016).CrossRefGoogle Scholar
Kim, C., Kim, H. and Chung, W. K., “Exactly Rao-Blackwellized unscented particle filters for SLAM,” IEEE International Conference on Robotics and Automation, Shanghai, China (2011) pp. 3589–3594.Google Scholar
Kim, C., Sakthivel, R. and Chung, W. K., “Unscented FastSLAM: A robust and efficient solution to the SLAM problem,” IEEE Trans. Robot. 24(4), 808–820 (2008).CrossRefGoogle Scholar
Julier, S., Uhlmann, J. and Durrant-Whyte, H. F., “A new method for the nonlinear transformation of means and covariances in filters and estimators,” IEEE Trans. Automat. Contr. 45(3), 477–482 (2000).CrossRefGoogle Scholar
Mehra, R. K., “On the identification of variances and adaptive Kalman filtering,” IEEE Trans. Automat. Contr. AC-15(2), 175–184 (1970).CrossRefGoogle Scholar
Fitzgerald, R. J., “Divergence of the Kalman filter,” IEEE Trans. Automat. Contr. AC-16(6), 736–747 (1971).CrossRefGoogle Scholar
Shi, Y., Han, C. and Liang, Y., “Adaptive UKF for target tracking with unknown process noise statistics,” The 12th International Conference on Information Fusion, Seattle, WA, July 6–9 (2009).Google Scholar
Jiang, Z., Song, Q., He, Y. and Han, J., “A novel adaptive unscented Kalman filter for nonlinear estimation,” The 46th IEEE Conference on Decision and Control, New Orleans, LA, December 12–14 (2007).Google Scholar
Havangi, R., Teshnelab, M., Nekoui, M. A. and Taghirad, H., “An adaptive Neuro-Fuzzy Rao-Blackwellized particle filter for SLAM,” Proceedings of the IEEE International Conference on Mechatronics, Istanbul, Turkey (2011) pp. 487–492.Google Scholar
Qiu, Z., Qian, H. and Wang, G., “Adaptive robust cubature Kalman filtering for satellite attitude estimation”, Chinese J. Aeronaut. 31(4), 806–819 (2017).CrossRefGoogle Scholar
Rasaratnam, I., Haykin, S. and Hurd, T. R., “Cubature Kalman filtering for continuous-discrete systems: Theory and simulations,” IEEE Trans. Signal Process. 58(10), 4977–4993 (2010).CrossRefGoogle Scholar
Kwak, N., Kim, G.W. and Lee, B.H., “A new compensation technique based on analysis of resampling process in FastSLAM,” Robotica 26(2), 205–217 (2008).CrossRefGoogle Scholar
Bolic, M., Djuric, P. M. and Hong, S., “Resampling algorithms and architectures for distributed particle filters,” IEEE Trans. Signal Process. 53(7), 442–2450 (2005).Google Scholar
Fen, C., Wang, M. and Ji, Q. B., “Analysis and comparison of resampling algorithms in particle filter,” J. Syst. Simul. 21(4), 1101–1105 (2009).Google Scholar
Havangi, R., “Intelligent FastSLAM: An intelligent factorized solution to simultaneous localization and mapping,” Int. J. Hum. Robot. 14(1), 1650026-1–1650026-20 (2017).CrossRefGoogle Scholar
Jian-min, D., Dan, L., Hong-Xiao, Y., and Hui, S., “An improved FastSLAM algorithm for autonomous vehicle based on the strong tracking square root central difference Kalman filter,” Proceedings of ITSC (2015), pp. 693–698.Google Scholar
Khairuddin, A. R. and Talib, M. S., “GA-PSO-FASTSLAM: A hybrid optimization approach in improving FastSLAM performance,” International Conference on Intelligent Systems Design and Applications, Vellore, India (2017).Google Scholar
Lv, T. and Feng, M., “An improved FastSLAM 2.0 algorithm based on FC&ASD-PSO,” Robotica 35(9), 1795–1815 (2017).Google Scholar
He, B., Ying, L., Zhang, S., Feng, X., Yan, T., Nian, R. and Shen, Y., “Autonomous navigation based on unscented-FastSLAM using particle swarm optimization for autonomous underwater vehicles,” Measurement 71, 89–101 (2015).CrossRefGoogle Scholar
wan Lee, S., Eoh, G. and Lee, B. H., “Relational FastSLAM: An improved Rao-Blackwellized particle filtering framework using particle swarm characteristics,” Robotica 34(6), 1282–1296 (2016).Google Scholar
Arasaratnam, I. and Haykin, S., “Cubature Kalman filters,” IEEE Trans. Automat. Contr. 54(6), 1254–1269 (2009).CrossRefGoogle Scholar
Arasaratnam, I., Haykin, S. and Hurd, R. T., “Cubature Kalman filtering for continuous-discrete system: Theory and simulations,” IEEE Trans. Signal Process. 58(10), 4977–4993 (2010).Google Scholar
Huang, Y. and Zhang, Y., “Robust student’s t-based stochastic cubature filter for nonlinear systems with heavy-tailed process and measurement noises,” IEEE Access 5, 7964–7974 (2017).CrossRefGoogle Scholar
Nishiyama, K., “Computational improvement of the fast H∞ filter based on information of input predictor,” IEEE Trans. Signal Process. 55(8), 4316–4320 (2007).CrossRefGoogle Scholar
Nishiyama, K., “An H∞ optimization and its fast algorithm for time-variant system identification,” IEEE Trans. Signal Process. 52(5), 1335–1342 (2004).CrossRefGoogle Scholar
Arulampalam, M. S., Maskell, S., Gordon, N. and Clapp, T., “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50(2), 174–188 (2002).CrossRefGoogle Scholar
Bar-Shalom, Y., Li, X. R. and Kirubarajan, T., Estimation with Applications to Tracking and Navigation (John Wiley & Sons, 2001).CrossRefGoogle Scholar
Nebot, E., “Victoria Park Data Set,” http://www-personal.acfr.usyd.edu.au/nebot/dataset.htm (2012).Google Scholar