Cui Yi, Zhou Fuqiang, Wang Yexin, Liu Liu, Gao He
Opt Express. 2014 Apr 21;22(8):9134-49. doi: 10.1364/OE.22.009134.
Binocular vision calibration is of great importance in 3D machine vision measurement. With respect to binocular vision calibration, the nonlinear optimization technique is a crucial step to improve the accuracy. The existing optimization methods mostly aim at minimizing the sum of reprojection errors for two cameras based on respective 2D image pixels coordinate. However, the subsequent measurement process is conducted in 3D coordinate system which is not consistent with the optimization coordinate system. Moreover, the error criterion with respect to optimization and measurement is different. The equal pixel distance error in 2D image plane leads to diverse 3D metric distance error at different position before the camera. To address these issues, we propose a precise calibration method for binocular vision system which is devoted to minimizing the metric distance error between the reconstructed point through optimal triangulation and the ground truth in 3D measurement coordinate system. In addition, the inherent epipolar constraint and constant distance constraint are combined to enhance the optimization process. To evaluate the performance of the proposed method, both simulative and real experiments have been carried out and the results show that the proposed method is reliable and efficient to improve measurement accuracy compared with conventional method.
双目视觉标定在三维机器视觉测量中具有重要意义。对于双目视觉标定而言，非线性优化技术是提高精度的关键步骤。现有的优化方法大多基于各自的二维图像像素坐标，旨在最小化两个相机的重投影误差之和。然而，后续的测量过程是在三维坐标系中进行的，这与优化坐标系不一致。此外，优化和测量的误差准则也不同。二维图像平面中相等的像素距离误差会导致相机前方不同位置处产生不同的三维度量距离误差。为了解决这些问题，我们提出了一种双目视觉系统的精确标定方法，该方法致力于最小化通过最优三角测量重建的点与三维测量坐标系中的地面真值之间的度量距离误差。此外，将固有的极线约束和恒定距离约束相结合以增强优化过程。为了评估所提方法的性能，进行了模拟实验和实际实验，结果表明，与传统方法相比，所提方法在提高测量精度方面可靠且高效。
