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arXiv · 2404.10687

Invariant Kalman Filtering with Noise-Free Pseudo-Measurements

Sven Goffin·Silvère Bonnabel·Olivier Brüls·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 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.

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Sven Goffin, Silvère Bonnabel, Olivier Brüls, Pierre Sacré. 2024-04-16. Invariant Kalman Filtering with Noise-Free Pseudo-Measurements. https://doi.org/10.1109/cdc49753.2023.10383262

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