针对在GPS（全球定位系统）失效的环境中无人旋翼直升机定位问题，提出基于视觉的迭代定位算法。采用改进的SIFT （尺度不变特征转换）图像匹配算法得到匹配点坐标，再经过坐标变换解算得到无人机三维坐标信息。改进SIFT算法中的关键点特征描述符由128维降低到64维，K-means聚类算法的应用减少了关键点的数目，从而减少了匹配时间而且去除了不必要的点，提高了匹配精度。算法实现阶段匹配点的选择利用了一幅图像匹配两次的思想，前后两次匹配结果中找到相同的特征点用于下一步的位置解算，使得算法能自动从大量特征点中找到所需要的点。仿真实验计算出无人机位置相对误差不超过4％，验证了定位算法的有效性和可靠性。%To solve the localisation problem of unmanned rotorcraft in GPS (globe positioning system)signals failure environment,we propose a vision-based iterative localisation algorithm.It finds the coordinates of matching points by using the improved SIFT (scale invariant feature transform)image matching algorithm,and then calculates UAV’s 3D coordinates information by solving coordinates transformation.In the improved SIFT the dimensions of key feature points descriptor are reduced from 1 28 to 64,and the application of k-means clustering algorithm decreases the amount of key features so that the matching time is reduced as well while those unnecessary points are also eliminated, thus the matching accuracy is improved.In the phase of algorithm implementation,the selection of matching points makes use of the idea of matching one image twice,from two matching results one after the other the same feature points are found for using in the next position solution,this enables the algorithm to find the required points from a great deal of feature points.Simulation experiment calculates that the relative error of UAV position is less than 4%,which validates the effectiveness and reliability of this localisation algorithm.
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