利用真实场景中的角结构约束进行相机自定标

首先讨论了真实场景中的角结构几何约束，然后利用这种几何约束提出了一种新的相机自定标算法，利用单幅透视投影图像中的角结构计算出相机的焦距、平移向量和旋转矩阵的初始值，然后利用两幅图像中的几何结构对相机内外参数进行优化，由于在求取相机参数初始值的时候只用到了一幅图像，这样就避免了在相机定标过程中可能出现的临界运动序列（critical motions sequence)，从而避免了临界运动序列引起的相机定标退化问题，提高了相机定标过程的鲁棒性，用两幅图像中的结构约束对初始值进行优化，进一步提高了结果的精确度，实验结果证明算法是强壮的。

Camera Self-Calibration Using the Corner Structure in Real Scenes

The geometric constraint, which is called corner structure, is studied in this paper Firstly, the corner structure in one image is introduced to calculate the initial solutions of the intrinsic and extrinsic parameters of the camera Then the solutions are optimized via minimizing the differences between the observed points and the reprojections under the geometric constraints in two images Because only one image is used when the initial solutions are computed, the critical motion sequences can be avoided, consequentially the degeneracies in camera self calibration can be avoided The precisions of the calculated parameters are improved because the geometric structures in two images are used in optimization The experimental results shown in this paper demonstrate that the algorithm is robust