Author: Wilson
Year: 2022
- Study eigen vector of covariance matrices of a BA = tangent space perturbation of the model
- Camera uncertainty computation in large 3D reconstruction seems interesting
- Fast and accurate camera covariance computation for large 3D reconstructions. seems even better
- Discussion about
$\Sigma_m$ (covariance of measurement), they choose$\Sigma_m = Diag(r(x))$ - Apply Schur Complement to get rid of 3D points parameters
- Discuss about defining a norm in
$\mathfrak{se}(3)$ that is not trivial, normalize the error in translation and in rotation using the average error in translation and rotation (important to have a good visualization, this is not an issue for us) - To avoid the inversion of the information matrix, they seek for the smallest eigenvectors of it (that should be the biggest of the cov)
- Vibrate each node (I don't understand this)
- This eigen value analysis doesn't allow to compare the accuracy of reconstructions