Abstract

We present a novel solution to compute the relative pose of a generalized camera. Existing solutions are either not general, have too high computational complexity, or require too many correspondences, which impedes an effifi- cient or accurate usage within Ransac schemes. We factorize the problem as a low-dimensional, iterative optimization over relative rotation only, directly derived from wellknown epipolar constraints. Common generalized cameras often consist of camera clusters, and give rise to omnidirectional landmark observations. We prove that our iterative scheme performs well in such practically relevant situations, eventually resulting in computational effificiency similar to linear solvers, and accuracy close to bundle adjustment, while using less correspondences. Experiments on both virtual and real multi-camera systems prove superior overall performance for robust, real-time multi-camera motion-estimation