A three-step camera calibration method
A three-step camera calibration method
复制标题
DOI：
10.1109/19.676732
复制
发表时间：
1997-10
影响因子：
5.6
通讯作者：
H. Bacakoglu;M. Kamel
中科院分区：
文献类型：
--
作者：
H. Bacakoglu;M. Kamel
摄像机标定是许多包含视觉传感的工业应用的关键问题。在本文中，我们计算的内在和外在的校准参数在三个步骤。在第一步中，使用线性最小二乘法近似校准参数。在第二步中，我们开发了两种替代配方，以获得一个最佳的旋转矩阵的校准参数计算的第一步。然后基于优化的旋转矩阵执行平移和透视变换的进一步优化。在第三步中，执行非线性优化以处理透镜失真。该非线性系统的解不仅使像点与对应世界坐标之间的透视变换关系最小化，而且满足旋转变换的正交性约束。为了评估我们所提出的方法的性能，计算和原始的4/spl倍/4齐次变换矩阵之间的误差矩阵的欧几里德范数被用作与现有方法进行比较的基础。应用该方法的仿真结果表明，非线性优化步骤之前和之后都有显着的改善。
Camera calibration is a crucial problem for many industrial applications that incorporate visual sensing. In this paper, we compute the intrinsic and extrinsic calibration parameters in three steps. In the first step, the calibration parameters are approximated using the linear least-squares method. In the second step, we develop two alternative formulations to obtain an optimal rotation matrix from the calibration parameters computed in the first step. Further optimization of translational and perspective transformations is then performed based on the optimized rotation matrix. In the third step, a nonlinear optimization is performed to handle lens distortion. The solution of the nonlinear system not only minimizes the perspective transformation relations between the image points and the corresponding world coordinates, but also satisfies the orthonormality constraints on the rotational transformation. To assess the performance of our proposed method, the Euclidean norm of the error matrix between the calculated and the original 4/spl times/4 homogeneous transformation matrices is used as a basis for comparison with existing methods. Simulation results from applying the method show significant improvements both before and after the nonlinear optimization step.