Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Robust recovery of the epipolar geometry for an uncalibrated stereo rig
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
International Journal of Computer Vision - Special issue on image-based servoing
Multiple view geometry in computer vision
Multiple view geometry in computer vision
A Mathematical Introduction to Robotic Manipulation
A Mathematical Introduction to Robotic Manipulation
Essential Matrix Estimation Using Gauss-Newton Iterations on a Manifold
International Journal of Computer Vision
Camera Displacement via Constrained Minimization of the Algebraic Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keeping features in the field of view in eye-in-hand visual servoing: a switching approach
IEEE Transactions on Robotics
Global Path-Planning for Constrained and Optimal Visual Servoing
IEEE Transactions on Robotics
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Estimating the camera pose in stereo vision systems is an important issue in computer vision and robotics. One popular way to handle this problem consists of determining the essential matrix which minimizes the algebraic error obtained from image point correspondences. Unfortunately, this search amounts to solving a nonconvex optimization, and the existing methods either rely on some approximations in order to get rid of the non-convexity or provide a solution that may be affected by the presence of local minima. This paper proposes a new approach to address this search without presenting such problems. In particular, we show that the sought essential matrix can be obtained by solving a convex optimization built through a suitable reformulation of the considered minimization via appropriate techniques for representing polynomials. Numerical results show the proposed approach compares favorably with some standard methods in both cases of synthetic data and real data.