Invariant Kalman Filtering with Noise-Free Pseudo-Measurements
Systems and Control
2024-04-17 v1 Systems and Control
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.
Keywords
Cite
@article{arxiv.2404.10687,
title = {Invariant Kalman Filtering with Noise-Free Pseudo-Measurements},
author = {Sven Goffin and Silvère Bonnabel and Olivier Brüls and Pierre Sacré},
journal= {arXiv preprint arXiv:2404.10687},
year = {2024}
}