Iterated Invariant Extended Kalman Filter (IterIEKF)

[en] We study the mathematical properties of the Invariant Extended Kalman Filter (IEKF) when iterating on the measurement update step, following the principles of the well-known Iterated Extended Kalman Filter. This iterative variant of the IEKF (IterIEKF) systematically improves its accuracy through Gauss-Newton-based relinearization, and exhibits additional theoretical properties, particularly in the low-noise regime, that resemble those of the linear Kalman filter. We apply the proposed approach to the problem of estimating the extended pose of a crane payload using an inertial measurement unit. Our results suggest that the IterIEKF significantly outperforms the IEKF when measurements are highly accurate.

Disciplines :

Electrical & electronics engineering

Author, co-author :

Goffin, Sven ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Robotique intelligente

Barrau, Axel

Bonnabel, Silvère

Bruls, Olivier ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Laboratoire des Systèmes Multicorps et Mécatroniques

Sacré, Pierre ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Robotique intelligente

Language :

English

Title :

Iterated Invariant Extended Kalman Filter (IterIEKF)

Publication date :

May 2026

Journal title :

IEEE Transactions on Automatic Control

ISSN :

0018-9286

Publisher :

Institute of Electrical and Electronics Engineers, United States - New Jersey

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

10.48550/arXiv.2404.10665

Funders :

F.R.S.-FNRS - Fonds de la Recherche Scientifique

Available on ORBi :

since 06 May 2024

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