Paper Info
Title
The Generalized Compressed Kalman Filter.
Abstract
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.
Year
DOI
Venue
2017
10.1017/S0263574716000369
ROBOTICA
Keywords
Field
DocType
CEKF,Compressed EKF,Compressed estimation,High dimensional estimation,Multi-agent SLAM
Alpha beta filter,Extended Kalman filter,Fast Kalman filter,Control theory,Unscented transform,Moving horizon estimation,Kalman filter,Engineering,Invariant extended Kalman filter,Ensemble Kalman filter
Journal
Volume
Issue
ISSN
35
8
0263-5747
Citations 
PageRank 
References 
1
0.36
6
Authors
1
Name
Order
Citations
PageRank
José E. Guivant1751103.39