针对工业环境下拍摄的大视场图像受噪声与畸变影响较大,使用传统匹配参数计算方法的精度无法满足现场测量需求的问题,提出一种基于非度量校正的大视场图像匹配参数标定算法。设计了一种共线特征点布置方案,基于非度量校正完成了特征点坐标的畸变校正,并提出基于该布置方案的特征点识别匹配算法,通过分区域抽样一致性法实现匹配参数的高精度标定。实验结果表明,该算法匹配精度相较于传统方法提高了51%以上,能够满足工业环境下大视场图像匹配参数所需的精度要求。

In order to solve the problem that images shot under industrial environment is considerably influenced by noise and distortion, precision of traditional algorithms for calibrating matching parameters cannot meet field measurement demand, a new method for calibrating large field of view image matching parameters based on non-metric correction is proposed. A layout plan of colinear feature points is designed, and based on non-metric correction, image coordinates of colinear feature points can be undistorted, which can be recognized and matched automatically with the aid of layout plan. Modified random sampling consensus algorithm based on regional division is proposed, and matching parameters can be calibrated. Experimental results show that matching precision is increased by 51% at least, which meets precision requirements under industrial environment.