A Unified Approach Combining Photometric and Geometric Information for Pose Estimation

In this paper, we present a novel approach for the relative pose estimation problem from point correspondences extracted from image pairs. Unlike classical algorithms, such as the Gold Standard algorithm, the proposed approach ensures that the matched points are photo-consistent throughout the pose estimation process. In fact, common algorithms use the photometric information to extract the feature points and to establish the 2D point correspondences. Then, they focus on minimizing, in a non-linear scheme, geometric distances between the projection of reconstructed 3D points and the coordinates of the extracted image points without taking the photometric information into account. The approach we propose in this paper merges geometric and photometric information in a unified cost function for the final non-linear minimization. This allows us to achieve results with higher precision and also with higher convergence frequency. Extensive experiments with ground truth on synthetic data show the superiority of the proposed approach in terms of robustness and precision. The simulation results have been confirmed by several tests on real image data.