A robust linear least-squares estimation of camera exterior orientation using multiple geometric features
For photogrammetric applications, solutions to camera exterior orientation problem can be classified into linear (direct) and non-linear. Direct solutions are important because of their computational efficiency. Existing linear solutions suffer from lack of robustness and accuracy partially due to the fact that the majority of the methods utilize only one type of geometric entity and their frameworks do not allow simultaneous use of different types of features. Furthermore, the orthonormality constraints are weakly enforced or not enforced at all. We have developed a new analytic linear least-squares framework for determining camera exterior orientation from the simultaneous use of multiple types of geometric features. The technique utilizes 2D/3D correspondences between points, lines, and ellipsecircle pairs. The redundancy provided by different geometric features improves the robustness and accuracy of the least-squares solution. A novel way of approximately imposing orthonormality constraints on the sought rotation matrix within the linear framework is presented. Results from experimental evaluation of the new technique using both synthetic data and real images reveal its improved robustness and accuracy over existing direct methods.
least-squares exterior orientation orthonormality constraints linear methods feature fusion