Pose Estimation using Point and Line Correspondences
he problem of a real-time pose estimation between a 3D scene and a single camera is a fundamental task in most 3D computer vision and robotics applications such as object tracking, visual Tser voing, and virtual reality. In this paper we present two fast methods for estimating the 3D pose using 2D to 3D point and line correspondences. The first method is based on the iterative use of a weak perspective camera model and forms a generalization of DeMenthon's method (1995) which consists of determining the pose from point correspondences. In this method the pose is iteratively improved with a weak perspective camera model and at convergence the computed pose corresponds to the perspective camera model. The second method is based on the iterative use of a paraperspective camera model which is a first order approximation of perspective. We describe in detail these two methods for both non-planar and planar objects. Experiments involving synthetic data as well as real range data indicate the feasibility and robustness of these two methods. We analyse the convergence of these methods and we conclude that the iterative paraperspective method has better convergence properties than the iterative weak perspective method. We also introduce a non-linear optimization method for solving the pose problem.