针对许多透镜成像畸变模型在求取像机参数时忽略透镜畸变中心位置的影响，将图像主点位置视为透镜畸变中心位置的问题，为提高标定精度，将畸变中心位置引入透镜畸变模型并作为增加的待标定像机参数。为弥补由于增加待求参数带来的标定过程运算量大、效率低等问题，采用随机并行梯度下降算法进行优化迭代以达到提高参数在线标定能力的目的。数值仿真和实物实验均表明,多参数随机并行梯度下降标定算法具有精度高、鲁棒性与快速收敛性等优点。

In camera parameter calculation, many distortion models ignore the effect of Center of Distortion (COD) and take the principal point or the center of the image as COD. In order to improve the precision of calibration result, the COD is introduced to the distortion model and added to the intrinsic parameters. To compensate for the problems of large computation burden and low efficiency due to the added parameter, Stochastic Parallel Gradient Descent (SPGD) is introduced to optimize the result. Results of digital simulation and experiments show that the SPGD based multi-parameter camera calibration method has high precision, fine robustness and can converge rapidly.