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本文给出了一种以空间不变量的数据来计算摄象机外部参数的方法．空间透视不变量是指在几何变换中如投影或改变观察点时保持不变的形状描述．由于它可以得到一个相对于外界来讲独立的物体景物的特征描述，故可以很广泛的应用到计算机视觉等方面．摄象机标定是确定摄象机摄取的２Ｄ图象信息及其３Ｄ实际景物的信息之间的变换关系，它包括内部参数和外部参数两个部分．内部参数表征的是摄象机的内部特征和光学特征参数，包括图象中心（Ｃｘ，Ｃｙ）坐标、图象尺度因子Ｓｘ、有效的焦距长度ｆ和透镜的畸变失真系数Ｋ；外部参数表示的是摄象机的位置和方向在世界坐标中的坐标参数，它包括平移矩阵Ｔ和旋转矩阵Ｒ３×３，一般情况下可以写成一个扩展矩阵［ＲＴ］３×４．本文基于空间透视不变量的计算数据，给出了一种标定摄象机外部参数的方法，实验结果表明该方法具有很强的鲁棒性．
This paper presents a method of calculating camera external parameters using spatially invariant data. Spatial perspective invariants are shape descriptions that remain the same in a geometric transformation, such as when projecting or changing an observation point. Because it can get a relative to the outside world in terms of the characteristics of the object description, it can be widely used in computer vision and so on. Camera calibration is to determine the relationship between the 2D image information captured by the camera and the information of its 3D real scene. It includes two parts: internal parameters and external parameters. The internal parameters characterize the internal and optical characteristics of the camera, including the image center (Cx, Cy) coordinates, the image scale factor Sx, the effective focal length f, and the lens distortion distortion factor K; the external parameters Is the coordinate of the position and orientation of the camera in world coordinates. It includes the translation matrix T and the rotation matrix R3 × 3, which can be written as an extended matrix [3 × 4] in general. This paper presents a method of calibrating the external parameters of a camera based on the computational data of invariant spatial perspectives. The experimental results show that the proposed method is robust.