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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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