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Conference Papers Year : 2024

Invariant Kalman Filtering with Noise-Free Pseudo-Measurements

Sven Goffin
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Silvère Bonnabel
Olivier Brüls
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Pierre Sacré
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Abstract

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 pseudomeasurements. 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.
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Dates and versions

hal-04398694 , version 1 (16-01-2024)

Identifiers

  • HAL Id : hal-04398694 , version 1

Cite

Sven Goffin, Silvère Bonnabel, Olivier Brüls, Pierre Sacré. Invariant Kalman Filtering with Noise-Free Pseudo-Measurements. 2023 IEEE 62nd Conference on Decision and Control (CDC), Dec 2023, Singapore (SG), Singapore. ⟨hal-04398694⟩
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