We study the relative orientation problem for two calibrated cameras with outliers from the feature matching. In recent years there has been a growing interest in optimal algorithms for computer vision. Most people agree that to get accurate solutions to multiview geometry problems, an appropriate norm of the reprojection errors should be minimized. To this end local as well as global optimization methods have been employed. To handle outliers though, heuristic methods still dominate the field. In this paper we address the problem of estimating relative orientation from uncertain feature correspondences. We formulate this task as an optimization problem and propose a branchand-bound algorithm to find the optimal set of correspondences as well as the optimal relative orientation. The approach is based on geometric constraints for pairs of correspondences. The experimental results are promising, especially for omnidirectional cameras. An implementation of the algorithm is also made publicly available to facilitate further research.
['Olof Enqvist', 'Fredrik Kahl']
Two View Geometry Estimation with Outliers