为了提升无人机飞行过程中的定位精度，以及使无人机正常飞行于全球定位系统（GPS）信号受到干扰或是失效的环境中，提出了基于机器视觉的无人机飞行过程中的定位算法。首先,基于尺度不变特征转换（SIFT）和KDTree搜索的图像匹配算法以及各坐标系之间的变换关系得到改进的坐标变换算法Trf1和Trf2，算法Trf1可求出目标匹配点的地面坐标，即建立无人机飞行环境地图，算法Trf2可计算出无人机实时位置；然后，在无人机飞行过程中利用算法Trf1和Trf2得出定位算法，并设计实验。实验表明，利用该算法计算出的无人机实时位置误差保持在12 cm的范围内，从而验证了算法的准确性。

In order to improve the localization accuracy for Unmanned Aerial Vehicle（UAV）in flighta localization algorithm is proposed based on machine vision.This algorithm can make UAV fly in the environment where Global Positioning System (GPS) signal is disturbed or is in failure.First of alltwo modified coordinate transformation algorithmsTrf1 and Trf2were obtained based on ScaleInvariant Feature Transform（SIFT）KDTree search image matching algorithm and the transformation between coordinate systems.With the help of Trf1the first coordinate transformation algorithmground coordinates of the target matched points were calculated out to build up a map of the flight environment.The second algorithmTrf2could calculate the realtime locations of UAV.Subsequentlythe localization algorithm in UAV flight was obtained based on the two modified coordinate transformation algorithmsand experiment was carried out based on the algorithms.Experiment results show that errors of calculated UAV positions are within 12 cmwhich demonstrates the accuracy of this method.