Research on multi-camera calibration and point cloud correction method based on three-dimensional calibration object. (April 2019)

• Record Type: Journal Article
• Authors: Huang, Lin; Da, Feipeng; Gai, Shaoyan
• Is Part Of: Optics and lasers in engineering. Volume 115(2019)
• Page Start: 32
• Page End: 41
• Publication Date: 2019-04
• Subjects: Calibration -- Multi-camera -- Measurement -- 3D calibration object -- Fringe projection; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2018.11.005
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: A simple 3D calibration object is designed which can easily be observed by multiple cameras. A correction method is proposed to minimum the error of reconstructed point clouds, improving the coincidence degree of point clouds in multiple angles. The method proposed can calibrate all the cameras in one-pass calibration process. The operation is simple and convenient. Correction is applied in 3D space instead of 2D space, the precision of point clouds is greatly improved. Abstract: The single camera measurement system can't obtain all the surface information of the object limited by the field of view, thus it can't achieve complete measurement of the object. Multi-camera system can overcome this difficulty. But it is difficult to unify the coordinate system of distinct cameras. In order to solve this problem, a global calibration method of multi-camera system is proposed. A three-dimensional cube calibration object is designed. Every camera is only calibrated on one surface of the cube calibration object. Because different surfaces of the cube calibration object are in a unified world coordinate, the coordinates of the feature points on different surfaces are naturally in the unified world coordinate system. Thus global calibration can be completed by calibrating multiple cameras at the same time. In addition, to minimize the error of calibration, a correction method is proposed. The correction parameters are obtained by the deviation between the world coordinates and the reconstructed coordinates of the feature points on calibration object. The parameters are applied to the measurement to improve the accuracy of the whole point cloud. Experiments are carried out on a multi-camera measurement system, and the results show that the method proposed in this paper is effective and feasible.