Pedro Miraldo

In this paper, we address the problem of pose estimation under the framework of generalized camera models. We propose a solution based on the knowledge of the coordinates of 3-D straight lines (expressed in the world coordinate frame) and their corresponding image pixels. Previous approaches used the knowledge of the coordinates of 3-D points (zero dimensional elements) and their corresponding images (zero dimensional elements). In this paper, pixels belonging to the image of 3-D lines are used. There is no need to establish correspondences between pixels and 3-D points. Correspondences are established between 3-D lines and their images. There is no need to identify individual pixels. The use of correspondences between pixels (that belong to the images of the 3-D lines) and 3-D lines facilitates the correspondence problem when compared to the use of world and image points. This is one of the contributions of this paper. The approach is both evaluated and validated using synthetic da...

ABSTRACT When considering non-central imaging devices, the computation of the relative pose requires the estimation of the rotation and translation that transform the 3D lines from one coordinate system to the second. In most of the state-of-the-art methods, this transformation is estimated by the computing a 6 × 6 matrix, known as the Generalized Essential Matrix. To allow a better understanding of this matrix, we derive some properties associated with its singular value decomposition.

ABSTRACT In this paper we study pose estimation for non-central cameras, using planes. The method proposed uses non-minimal data. Using the homography matrix to represent the transformation between the world and camera coordinate systems, we describe a non-iterative algorithm for pose estimation. In addition, we propose a parameter optimization to refine the pose estimate. We evaluate the proposed solutions against the state-of-the-art method in terms of both robustness to noise and computation time. From the experiments, we conclude that the proposed method is more accurate against noise. We also conclude that the numerical results obtained with this method improve with increasing number of data points. In terms of processing speed both versions of the algorithm presented are faster than the state-of-the-art algorithm.