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The Generalized Compressed Kalman Filter

Published online by Cambridge University Press:  23 June 2016

Jose E. Guivant
Jose E. Guivant*
Affiliation:
School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney 2052, NSW, Australia
*
*Corresponding author. E-mail: j.guivant@unsw.edu.au
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Summary

This paper presents a generalization of the Compressed Extended Kalman Filter (CEKF) by introducing the definition of the compressed operation for Bayesian estimation processes and the definition of the Generalized Compressed Kalman Filter (GCKF). The GCKF extends the original definition of the CEKF, formerly introduced for treating a family of estimation problems, such as the Simultaneous Localization and Mapping (SLAM). The new proposed approach, the GCKF, is capable of treating problems that are more general than those of the original CEKF, such as estimation processes that involve certain stochastic Partial Differential Equation (SPDE) models. In addition to solving the SLAM and multi-agent SLAM problem in an efficient way, the GCKF can be applied to more general cases. As a proof of the capabilities of the new method for solving practical estimation problems, a multi-agent SLAM process is presented in the experimental section of this paper.

Keywords

CEKFCompressed EKFCompressed estimationHigh dimensional estimationMulti-agent SLAM
Type
Articles
Information
Robotica , Volume 35 , Issue 8 , August 2017 , pp. 1639 - 1669
Copyright
Copyright © Cambridge University Press 2016 

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References

1. Bailey, T. and Durrat-White, H., “Simultaneous localization and mapping (SLAM): Part I,” IEEE Robot. Autom. Mag. 13 (2), 99–110 (2006).Google Scholar
2. Bailey, T. and Durrat-White, H., “Simultaneous localization and mapping (SLAM): Part II,” IEEE Robot. Autom. Mag. 13 (3), 108–117 (2006).Google Scholar
3. Williams, S., Efficient Solutions to Autonomous Mapping and Navigation Problems Ph.D. Thesis (Sydney: The University of Sydney, 2001).Google Scholar
4. Nebot, E., Lee, G. K. F. and Brubaker, T. A., “Experiments On a Single-Link Flexible Manipulator,” In: Proceedings of the USA-Japan Symposium on Flexible Automation, Minneapolis, USA (1988).Google Scholar
5. Barton, M., “Evaluating the performance of kalman-filter-based EEG source localization,” IEEE Trans. Biomed. Eng. 56 (1), 122–136 (2009).Google Scholar
6. Lopez, J. and Espinosa, J., “Spatio-Temporal EEG Brain Imaging Based on Reduced Kalman Filtering,” In: Proceedings of the IEEE/EMBS International Conference on Neural Engineering (NER), Cancun, Mexico (2011).Google Scholar
7. Julier, S. and Uhlmann, J., “Unscented filtering and nonlinear estimation,” Proc. IEEE 92 (3), 401–422 (2004).Google Scholar
8. Guivant, J. and Nebot, E., “Optimization of the simultaneous localization and map-building algorithm for real-time implementation,” IEEE Trans. Robot. Autom. 17 (3), 242–257 (2001).Google Scholar
9. Guivant, J., Efficient Simultaneous Localization and Mapping in Large Environments Ph.D. Thesis (The University of Sydney, Sydney, Australia, 2002).Google Scholar
10. Guivant, J., “The Compressed Extended Kalman Filter,” SLAM Summer School, Stockholm, Sweden (May 2002). [Online]. Available: www.cas.kth.se/SLAM/Presentations/cekf.pdf. [Accessed 25 Jul. 2015].Google Scholar
11. Knight, J., Davison, A. and Reid, I., “Towards Constant Time SLAM Using Postponement,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, USA (2001).Google Scholar
12. Lin, J., Xie, L. and Xiao, W., “Target tracking in wireless sensor networks using compressed Kalman filter,” Int. J. Sensor Netw. 6 (3–4), 251–262 (2009).CrossRefGoogle Scholar
13. Cheng, J. and Kim, J., “Compressed Unscented Kalman Filter-Based SLAM,” In: Proceedings of the International Conference on Robotics and Biomimetics, Bali, Indonesia (2014).Google Scholar
14. Evensen, G., “The Ensemble Kalman Filter: theoretical formulation and practical implementation,” Ocean Dyn. 53 (4), 343–367 (2003).Google Scholar
15. Houtekamer, P. L. and Mitchell, H. L., “Data assimilation using an ensemble kalman filter technique,” Mon. Weather Rev. 126 (3), 796–811 (1998).2.0.CO;2>CrossRefGoogle Scholar
16. Houtekamer, P. L. and Mitchell, H. L., “Ensemble kalman filtering,” Q.J.R. Meteorol. 131 (613), 3269–3289 (2006).Google Scholar
17. Roumeliotis, S. and Burdick, J., “Stochastic Cloning: A Generalized Framework for Processing Relative State Measurements,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Washington DC, USA (2002).Google Scholar
18. Mourikis, A., Roumeliotis, S. and Burdick, J., “SC-KF Mobile Robot Localization: A Stochastic Cloning Kalman Filter for Processing Relative-State Measurements,” IEEE Trans. Robot. 23 (4), (2007).CrossRefGoogle Scholar
19. Julier, S., “The stability of covariance inflation methods for SLAM,” In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Las Vegas, USA (2003).Google Scholar
20. Guivant, J. and Nebot, E., “Solving computational and memory requirements of feature-based simultaneous localization and mapping algorithms,” IEEE Trans. Robot. Autom. 19 (4), 749–755 (2003).Google Scholar
21. Neira, J. and Tardos, J. D., “Data association in stochastic mapping using the joint compatibility test,” IEEE Trans. Robot. Autom. 17 (6), 890–897 (2001).Google Scholar
22. Guivant, J., “Addtional Figures and Data for GCKF-Robotica,” Sep. 2015. [Online]. Available: www.Possumrobot.com/GCKF. [Accessed 25 Sep. 2015].Google Scholar
23. Baneen, U. and Guivant, J., “Damage detection in fiber-reinforced composite beams by using a bayesian fusion method,” J. Vib. Acoust. 135 (6), 061008 (2013).Google Scholar
24. Paz, L., Neira, J. and Tardos, J., “Divide and Conquer: EKF SLAM in O(n),” IEEE Trans. Robot. 24 (5), 1107–1120 (2008).Google Scholar
25. Cheng, J., Kim, J., J. Shao and W. Zhang, “Robust linear pose graph-based SLAM,” Robot. Auton. Syst. 72, 71–82 (2015).CrossRefGoogle Scholar