基于CenSurE-star特征的无人机景象匹配算法

UAV scene matching algorithm based on Cen Sur E-star feature

针对传统局部不变特征的景象匹配算法冗余点多、实时性差、抗几何变换不突出的情况,提出基于CenSurE-star的无人机(UAV)景象匹配算法。首先采用Cen Sur E特征星型滤波器(CenSurE-star)提取基准图和实时图中的特征点,并生成FREAK二进制描述符;然后将汉明距离作为特征点的相似性判定度量,采用K近邻距离比值的方法提取匹配点对;最后利用基于RANSAC的定位模型得到空间几何变换关系,实现图像匹配并获取定位点经纬坐标。算法性能评价实验表明,本文算法不仅相对于SIFT、SURF、ORB算法,对各种变换具有更好的鲁棒性,而且相对于改进的SIFT、SURF算法处理时间有更大程度的缩短,算法定位误差在0.8个像素内,尺度误差在0.02倍内,旋转角度误差在0.04°内。基于算法进行外场飞行实验,实验证明算法定位精度较高,可以适应地貌信息较少的环境,并能满足无人机视觉辅助导航的需求。

Aiming at the problems of large number of redundant points,poor real-time performance and low ability to resist geometric transformations for the scene matching algorithm based on traditional local invariant feature,an unmanned aerial vehicle（UAV） scene matching algorithm based on Cen Sur E-star is proposed.Firstly,the Cen Sur E-star filter is adopted to extract the feature points in the reference image and real-time image,and then FREAK binary descriptors are generated.Secondly,the Hamming distance is taken as the similarity measurement of the feature points,and the K-Nearest Neighbor distance ratio method is used to extract the matching point pairs.Finally,the positioning model based on RANSAC is utilized to obtain the space geometric transformation relations,the image matching is achieved,and the latitude and longitude coordinates of the positioning points are obtained.The algorithm performance evaluation experiments show that compared with SIFT,SURF and ORB algorithms,the proposed algorithm has better robustness in dealing with various image transformations; and compared with the improved SIFT and SURF algorithms,the processing time of the proposed algorithm is greatly shortened.The positioning error of the algorithm is within 0.8 pixels,the scale error is within 0.02 times,and the rotation angle error is within 0.04 degrees.Based on the proposed algorithm,a field flight experiment was conducted.The experiment results prove that the proposed algorithm has high positioning accuracy,can adapt to the environment with less landform information,and meets the requirements of UAV vision aided navigation.