Essential Matrix Estimation Using Gauss-Newton Iterations on a Manifold

A novel approach for essential matrix estimation is presented, this being a key task in stereo vision processing. We estimate the essential matrix from point correspondences between a stereo image pair, assuming that the internal camera parameters are known. The set of essential matrices forms a smooth manifold, and a suitable cost function can be defined on this manifold such that its minimum is the desired essential matrix. We seek a computationally efficient optimization scheme towards meeting the demands of on-line processing of video images. Our work extends and improves the earlier research by Ma et al., who proposed an intrinsic Riemannian Newton method for essential matrix computations. In contrast to Ma et al., we propose three Gauss-Newton type algorithms that have improved convergence properties and reduced computational cost. The first one is based on a novel intrinsic Newton method, using the normal Riemannian metric on the manifold consisting of all essential matrices. The other two methods are Newton-like methods, that are more efficient from a numerical point of view. Local quadratic convergence of the algorithms is shown, based on a careful analysis of the underlying geometry of the problem.