Abstract

Machine vision techniques, including camera calibration methods, are of great importance for the development of vision-based measurements. However, in multi-camera calibration methods, rapidly constructing accurate geometric relationships among different coordinates is very difficult. Herein, we present a multi-camera calibration method capable of calibrating the intrinsic and extrinsic parameters of four cameras using only a single captured image per camera. Unlike Zhang’s method, which relies on multiple captured images to calibrate the cameras, the method uses a multi-plane stereo target containing multiple fixed planes to which coded patterns are attached. This target greatly reduces the time required for calibration and improves calibration robustness. The proposed method was experimentally compared with traditional camera calibration. The problem affecting the calibration accuracy in single calibration of multiple cameras is that the feature points on the captured images produce occlusion or different degrees of blurring; in the calibration of multiple cameras multiple times, the error accumulation caused by the calibration of two adjacent cameras is solved. This demonstration of a multi-camera calibration method improves camera calibration and provides a new design philosophy, to the best of our knowledge, for machine vision and vision-based measurement.

© 2019 Optical Society of America

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