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.
Contact- aided invariant extended Kalman filtering for robot state estimation,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
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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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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.