Calibration method and experiments of multi-camera's parameters based on freely moving one-dimensional calibration object

Abstract: The one-dimensional (1D) calibration method is of easy impl e entation and with high efficiency. In order to overcome deficiencies of existing 1D calibration methods, a new method is proposed in this paper to calibrate the interna l and external parameters of multiple cameras by using a 1D cal ibration object. Firstly, the pair-wise calibration is car ried out, where the positions of principal points are assumed to b e approximately known and only those parameters such as the distortion, focal length, relative rotation and transl tion are needed to be taken into consideration. Then, with t he fundamental matrix and geometry constraints of feature poi nts on the calibration wand, the internal and external param eters of binocular cameras can be determined. After the pair-wise calibration, all parameters (including the positions of pr incipal points) of the multicamera system are refined by using Dijkst ra’s shortest path algorithm and the bundle adjustment meth od. The simulation and real experiments demonstrated the effec tiveness of the proposed method.

[1]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[2]  Zuren Feng,et al.  Accurate calibration of stereo cameras for machine vision , 2004 .

[3]  Max Q.-H. Meng,et al.  A new camera calibration method for multi-camera localization , 2010, 2010 IEEE International Conference on Automation and Logistics.

[4]  Zeyu Li,et al.  Wide-area external multi-camera calibration using vision graphs and virtual calibration object , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

[5]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Fumiaki Tomita,et al.  Plane-based calibration algorithm for multi-camera systems via factorization of homography matrices , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  Tomás Svoboda,et al.  A Convenient Multicamera Self-Calibration for Virtual Environments , 2005, Presence: Teleoperators & Virtual Environments.

[8]  B. Siciliano,et al.  Eye-in-Hand/Eye-to-Hand Multi-Camera Visual Servoing , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[9]  Manolis I. A. Lourakis.  Sparse Non-linear Least Squares Optimization for Geometric Vision , 2010, ECCV.

[10]  Zhang Xuebo.  A global exponential observer for camera calibration , 2011 .

[11]  Zhang Guangjun.  CORRECTING DISTORTION OF CAMERA LENS WITH COLLINEAR POINTS , 2006 .

[12]  Jingchao Chen.  Dijkstra's Shortest Path Algorithm , 2003 .

[13]  Koichi Hashimoto,et al.  A review on vision-based control of robot manipulators , 2003, Adv. Robotics.

[14]  Liang Wang.  Multi-Camera Calibration Based on 1D Calibration Object , 2007 .

[15]  F. Yuan,et al.  Vision guided robots for automated assembly , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[16]  Hyondong Oh,et al.  Indoor UAV Control Using Multi-Camera Visual Feedback , 2011, J. Intell. Robotic Syst..