Finding the position and orientation of a sensor on a robot manipulator using quaternions
International Journal of Robotics Research
International Journal of Robotics Research
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical Polynomial Algebra
How Hard is 3-View Triangulation Really?
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Structure from Motion with Wide Circular Field of View Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ideals, Varieties, and Algorithms: An Introduction to Computational Algebraic Geometry and Commutative Algebra, 3/e (Undergraduate Texts in Mathematics)
A Minimal Solution to the Generalised 3-Point Pose Problem
Journal of Mathematical Imaging and Vision
Automatic Generator of Minimal Problem Solvers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Omnidirectional Image Stabilization for Visual Object Recognition
International Journal of Computer Vision
Structure-from-motion based hand-eye calibration using L$_8$ minimization
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A branch-and-bound algorithm for globally optimal hand-eye calibration
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
| Hi-index | 0.00 |
In this paper we solve the problem of estimating the relative pose between a robot's gripper and a camera mounted rigidly on the gripper in situations where the rotation of the gripper w.r.t. the robot global coordinate system is not known. It is a variation of the so called hand-eye calibration problem. We formulate it as a problem of seven equations in seven unknowns and solve it using the Gröbner basis method for solving systems of polynomial equations. This enables us to calibrate from the minimal number of two relative movements and to provide the first exact algebraic solution to the problem. Further, we describe a method for selecting the geometrically correct solution among the algebraically correct ones computed by the solver. In contrast to the previous iterative methods, our solution works without any initial estimate and has no problems with error accumulation. Finally, by evaluating our algorithm on both synthetic and real scene data we demonstrate that it is fast, noise resistant, and numerically stable.