@INPROCEEDINGS{Botterilletal2011,
author = {Tom Botterill and Steven Mills and Richard Green},
title = {Fast RANSAC hypothesis generation for essential matrix estimation},
booktitle = {In Proceedings of the International Conference on Digital
Image Computing: Techniques and Applications (DICTA)},
year = {2011},
abstract={The RANSAC framework is often used to estimate the relative pose of two cameras from outliercontaminated point correspondences, via the essential matrix, however this is computationally expensive due the cost of computing essential matrices from many sets of five to seven correspondences. The leading contemporary 5point solver (Nister, 2004) is slow because of the expensive linear algebra decompositions and polynomial solve which are required. To avoid these costs we propose to use LevenbergMarquardt optimisation on a manifold to find a subset of the compatible essential matrices. The proposed algorithm finds essential matrices at a higher rate than closedform approaches, and reduces the time needed to find relative poses using RANSAC by 25%. The second contribution of this paper is to apply the optimisations used in 5point solvers to the classic 7point algorithm. RANSAC using the optimised 7point algorithm is considerably faster than 5point RANSAC (unless planar point configurations are common), despite the increased number of iterations necessary.}
}
