3D double-vision inspection is very necessary. It has a larger field of view, and can solve the problem of “blind area” for 3D measurement, as proposed by 3D single-vision inspection. At the beginning of this paper, the principle of structured-light based 3D vision inspection is introduced. Then, a method of gaining calibration points for 3D double-vision inspection system is proposed in detail. In order to gain calibration points with high precision, a double-directional photoelectric aiming device is designed as well, and a method for compensating the position-setting error of the aiming device is described. The coordinates of all calibration points are precisely unified in a world coordinate system. The application of RBF (radial basis function) neural network in establishing the inspection model of structured-light based 3D vision is described in detail. Finally, with the use of the calibration points, the inspection model of 3D double-vision based on RBF neural network is successfully established. The model’s training accuracy is 0.078 mm, and the testing accuracy is 0.084 mm.
3D Double-Vision Inspection Based on Structured Light
Contributed by the Manufacturing Engineering Division for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received October 2001; Revised July 2002. Associate Editor: E. C. DeMeter.
- Views Icon Views
- Share Icon Share
- Cite Icon Cite
- Search Site
Zhang, G., Wei, Z., and Li, X. (July 23, 2003). "3D Double-Vision Inspection Based on Structured Light ." ASME. J. Manuf. Sci. Eng. August 2003; 125(3): 617–623. https://doi.org/10.1115/1.1557292
Download citation file:
- Ris (Zotero)
- Reference Manager