MLESAC: A new robust estimator with application to estimating image geometry

A new method is presented for robustly estimating multiple view relations f rom point correspondences. The method comprises two parts. The first is a n ew robust estimator MLESAC which is a generalization of the RANSAC estimato r. It adopts the same sampling strategy as RANSAC to generate putative solu tions, but chooses the solution that maximizes the likelihood rather than j ust the number of inliers. The second part of the algorithm is a general pu rpose method for automatically parameterizing these relations, using the ou tput of MLESAC. A difficulty with multiview image relations is that there a re often nonlinear constraints between the parameters, making optimization a difficult task. The parameterization method overcomes the difficulty of n onlinear constraints and conducts a constrained optimization. The method is general and its use is illustrated for the estimation of fundamental matri ces, image-image homographies, and quadratic transformations. Results are g iven for both synthetic and real images. It is demonstrated that the method gives results equal or superior to those of previous approaches, (C) 2000 Academic Press.