NANO-L is a natural-gradient Gaussian approximation filter on Lie groups that avoids linearization by optimizing multiplicative increments via the exponential map, yielding exact covariance updates for invariant models and 40% lower error on hardware.
Unscented filtering and nonlinear estimation,
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
CAR-EnKF reduces RMSE versus standard EnKF by recalibrating the Kalman gain effect and adding an online-tuned covariance compensation term that activates only under measurement nonlinearity.
Two new techniques guarantee positive definiteness in the NANO filter's natural gradient covariance update, improving performance over standard Kalman filters on nonlinear systems.
The NANO filter uses natural gradient descent to iteratively refine Gaussian state estimates while preserving covariance positive definiteness and exactly recovering the Kalman update in the linear-Gaussian case.
citing papers explorer
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Natural Gradient Gaussian Approximation Filter on Lie Groups for Robot State Estimation
NANO-L is a natural-gradient Gaussian approximation filter on Lie groups that avoids linearization by optimizing multiplicative increments via the exponential map, yielding exact covariance updates for invariant models and 40% lower error on hardware.
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CAR-EnKF: A Covariance-Adaptive and Recalibrated Ensemble Kalman Filter Framework
CAR-EnKF reduces RMSE versus standard EnKF by recalibrating the Kalman gain effect and adding an online-tuned covariance compensation term that activates only under measurement nonlinearity.
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Natural Gradient Gaussian Approximation Filter with Positive Definiteness Guarantee
Two new techniques guarantee positive definiteness in the NANO filter's natural gradient covariance update, improving performance over standard Kalman filters on nonlinear systems.
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Natural Gradient Bayesian Filtering: Geometry-Aware Filter for Dynamical Systems
The NANO filter uses natural gradient descent to iteratively refine Gaussian state estimates while preserving covariance positive definiteness and exactly recovering the Kalman update in the linear-Gaussian case.