Invariant Kalman Filtering with Noise-Free Pseudo-Measurements - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Invariant Kalman Filtering with Noise-Free Pseudo-Measurements

Résumé

In this paper, we focus on developing an Invariant Extended Kalman Filter (IEKF) for extended pose estimation for a noisy system with state equality constraints. We treat those constraints as noise-free pseudo-measurements. To this aim, we provide a formula for the Kalman gain in the limit of noise-free measurements and rank-deficient covariance matrix. We relate the constraints to group-theoretic properties and study the behavior of the IEKF in the presence of such noise-free measurements. We illustrate this perspective on the estimation of the motion of the load of an overhead crane, when a wireless inertial measurement unit is mounted on the hook.

Mots clés

Fichier principal
Vignette du fichier
Invariant_Kalman_Filtering_with_Noise-Free_Pseudo-Measurements.pdf (380.07 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04409333 , version 1 (22-01-2024)

Identifiants

Citer

Sven Goffin, Silvère Bonnabel, Olivier Brüls, Pierre Sacré. Invariant Kalman Filtering with Noise-Free Pseudo-Measurements. 2023 62nd IEEE Conference on Decision and Control (CDC), Dec 2023, Singapore, Singapore. pp.8665-8671, ⟨10.1109/cdc49753.2023.10383262⟩. ⟨hal-04409333⟩
2 Consultations
8 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More