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Invariant Kalman Filtering with Noise-Free Pseudo-Measurements
- Source :
- 62nd IEEE Conference on Decision and Control (CDC), Singapore, Singapore, 2023, pp. 8665-8671
- Publication Year :
- 2024
-
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.
- Subjects :
- Electrical Engineering and Systems Science - Systems and Control
Subjects
Details
- Database :
- arXiv
- Journal :
- 62nd IEEE Conference on Decision and Control (CDC), Singapore, Singapore, 2023, pp. 8665-8671
- Publication Type :
- Report
- Accession number :
- edsarx.2404.10687
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/CDC49753.2023.10383262