一种基于卫星图像匹配的无人机自主定位算法

A UAV Autonomous Positioning Algorithm Based on Satellite Image Matching

针对基于图象匹配的无人机自主定位易失效的问题,文章提出了一种基于卫星图像辅助的由粗到精的无人机自主定位框架。首先设计了一种CNN-LSTM联合分类器,利用CNN网络抽取图像特征的优势,结合无人机图像时间维度上的连续性,实现无人机图像与卫星基准图像的区域匹配;然后从匹配后的区域提取同名点,结合光学成像的共线约束,设计了一种基于多点加权约束的无人机自主定位算法。试验结果表明提出的算法鲁棒性好、精度高,适合用于无人机自主定位。

For the problem that autonomous positioning of UAVs based on image matching is easily invalid,the paper proposed a coarse-to-fine autonomous UAV positioning framework based on the assist by satellite images.Firstly,a CNN-LSTM joint classifier is designed to achieve regional matching between UAV image and satellite reference image,utilizing the advantage of CNN network to extract image features and combining with the continuity of UAV image in time dimension.Then,an autonomous UAV positioning algorithm based on multi-point weighted constraints is designed by extracting points of the same name from the matched region and combining with the collinear constraint of optical imaging.The experimental results show that this proposed algorithm is suitable for UAV autonomous positioning with good robustness and high precision.