Marginalizing and Conditioning Gaussians onto Linear Approximations of Smooth Manifolds with Applications in Robotics

Derives Gaussian marginalization and conditioning on general linear manifolds. Linearization extends the method to smooth manifolds, with covariance-estimation applications in SLAM and constrained optimization.

Derives Gaussian marginalization and conditioning on general linear manifolds. Linearization extends the method to smooth manifolds, with covariance-estimation applications in SLAM and constrained optimization.

When reading the original paper, identify its problem, inputs, outputs, and methodological assumptions.

Derives Gaussian marginalization and conditioning on general linear manifolds. Linearization extends the method to smooth manifolds, with covariance-estimation applications in SLAM and constrained optimization.

Check the baselines, datasets, evaluation protocol, and ablations in the original paper. This directory overview does not provide an independent evaluation or experimental results table.