Pose estimation from video sequences based on sylvesters equation In this paper, we introduce a method to jointly track the object motion and estimate pose within the framework of particle filtering. We focus on direct estimation of the 3D pose from a 2D image sequence. ScaleInvariant Feature Transform (SIFT) is used to extract feature points in the images. We show that pose estimation from the corresponding feature points can be formed as a solution to Sylvesteru0027s equation. We rely on a solution to Sylvesteru0027s equation based on the Kronecker product method to solve the equation and determine the pose state. We demonstrate that the classical Singular Value Decomposition (SVD) approach to pose estimation provides a solution to Sylvesteru0027s equation in 3D3D pose estimation. The proposed approach to the solution of Sylvesteru0027s equation is therefore equivalent to the classical SVD method for 3D3D pose estimation, yet it can also be used for pose estimation from 2D image sequences. Finally, we rely on computer simulation experiments to demonstrate the performance of our algorithm on video sequences.

Linebased Absolute and Relative Camera Pose Estimation in Structured Environments 3D lines in structured environments encode particular regularity like parallelism and orthogonality. We leverage this structural regularity to estimate the absolute and relative camera poses. We decouple the rotation and translation, and propose a novel rotation estimation method. We decompose the absolute and relative rotations and reformulate the problem as computing the rotation from the Manhattan frame to the camera frame. To compute this rotation, we propose an accurate and efficient twostep method. We first estimate its two degrees of freedom (DOF) by two image lines, and then estimate its third DOF by another image line. For these lines, we assume their associated 3D lines are mutually orthogonal, or two 3D lines are parallel to each other and orthogonal to the third. Thanks to our twostep DOF estimation, our absolute and relative pose estimation methods are accurate and efficient. Moreover, our relative pose estimation method relies on weaker assumptions or less correspondences than existing approaches. We also propose a novel strategy to reject outliers and identify dominant directions of the scene. We integrate it into our pose estimation methods, and show that it is more robust than RANSAC. Experiments on synthetic and realworld datasets demonstrated that our methods outperform stateoftheart approaches.