@INPROCEEDINGS{Botterill-etal-2011c,

author = {Tom Botterill and Steven Mills and Richard Green},

title = {Refining essential matrix estimates from RANSAC},

booktitle = {In Proceedings of Image and Vision Computing New Zealand},

year = {2011},
abstract={To estimate the relative pose of two cameras from

outlier-contaminated feature correspondences, the essential matrix

and inlier set is estimated using RANSAC, then this estimate

is refined to minimise an error function on the correspondences.

This paper evaluates several refinement methods which minimise

functions of Sampson’s error. All perform well on large sets of

correspondences or when inlier rates are high, but many perform

poorly or fail when the inlier set found by RANSAC is small; this

is shown to be because the inlier sets contain remaining outliers,

while missing some inliers.

The most accurate solutions are given by minimising the robust

Blake-Zisserman function of Sampson’s error, although this

provides only a minimal improvement in accuracy compared with

least squares refinement. The most reliable results are given by

nonlinear optimisation constrained to the essential manifold. An

efficient parametrisation of the essential manifold as a quaternion

and a unit vector is described; applying Iteratively Reweighted

Least Squares combined with Levenberg-Marquardt optimisation

on this manifold takes typically less than one millisecond..}

}