一种新的基于Kruppa方程的摄像机线性自标定方法

A novel camera linear self-calibration technique based on the Kruppa equations

针对非线性优化求解Kruppa方程进行摄像机自标定的局部最优问题,提出了一种在特殊情况下的基于Kruppa方程的线性自标定算法.当摄像机在圆周上运动时,首先根据外极线约束关系得到较准确的基本矩阵,然后根据Kruppa方程的未知系数与基本矩阵奇异值分解的参数关系求解摄像机的内外参数.实验结果表明,所得结论和方法是正确和有效的.

The Kruppa equation-based camera self-calibration methods using nonlinear optimization are easily stuck in some local minimum. A new method is presented for the linearization of the Kruppa equation under a special case where the camera rotates along the circle path. For the case, the fundamental matrix is firstly computed through epipolar constraint, then the intrinsic and extrinsic parameters of camera are computed by the unknown scale in equation which is represented using the singular value decomposition （SVD）-based factorization result of the fundamental matrix. Experimental results validate the correctness of the proposed method.