【摘要】 针对大尺寸3-D视觉测量系统中参数校准精度及特征点质心提取准确性均较低的问题,提出了一种基于五点交比不变性约束的质心提取算法。在高斯曲面多次拟合算法及规则特征点阵的基础上,引入同一空间直线上五点交比值不变性,以此作为约束条件提取特征点的质心;并利用Levenberg-Marquardt(L-M)迭代算法对质心坐标进行优化,由此获得特征点质心坐标的最优解;再结合更精确的特征点质心坐标,校准摄像机内、外参数,以达到提高视觉系统参数校准精度及质心提取精度的目的。实验结果表明,与单纯的高斯曲面多次拟合算法、2×2方块模板法相比,本文的质心提取算法在精度和稳定性方面均有其优势。更多还原

【Abstract】 To solve the problems that both parameter calibration precision and centroid extraction accuracy of feature points are lower in large-scale 3D vision measurement systems,a centroid extraction algorithm based on the restriction of five-point cross-ratio invariance is proposed.Basd on a Gaussian surface polynomial fitting algorithm and regular feature-point array,the fivepoint cross-ratio invariance on the same spatial straight line is introduced and used as the restriction condition,to extract centroids of feature points.The Levenberg-Marquardt iterative algorithm is utilized to optimize the centroid coordinates and then optimum solution of all points,are obtained lombined with more precise centroid coordinates of feature points,intrinsic and extrinsic camera parameters are calibrated so as to enhance the parameter calibration and centroid extraction precisions in vision measurement systems.The experimental results prove that the proposed centroid extraction algorithm improves both centroid extraction accuracy and stability,compared with the Gaussian surface polynomial fitting algorithm and the 2×2 quadrel algorithm.更多还原