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.
The invariant extended Kalman filter as a stable observer
2 Pith papers cite this work. Polarity classification is still indexing.
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Surveys signal-processing principles for magnetic-field-based localization, presenting key technologies in a common parametric framework and identifying research challenges.
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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.