为了满足无人机航拍对图像拼接实时性和稳健性的要求,提出了一种将改进的快速特征点提取和描述(ORB)算法与渐进一致采样(PROSAC)算法相结合的无人机航拍图像拼接算法。首先,利用加速稳健性特征(SURF)算法检测特征点,利用具有旋转特性的二进制稳健基元独立特征(rBRIEF)算法描述特征点,接着利用双向匹配算法和最近邻距离比率策略进行特征点的粗匹配,利用PROSAC算法剔除错误的匹配;然后利用全局单应性变换模型进行图像配准,最后利用渐入渐出图像融合方法进行图像的无缝融合拼接。实验结果表明:该算法在精度和速度上达到很高的平衡,能实现又快又好的图像拼接。

To meet the requirements of real-time and robust image stitching of unmanned aerial vehicle (UAV) aerial photography, this paper proposes an aerial image stitching algorithm for UAVs based on an improved fast feature-point extraction and description (ORB) algorithm combined with a progressive sample consensu (PROSAC) algorithm. First, the feature points are detected by the speeded up robust feature (SURF) algorithm and described by the rotation-aware binary robust independent elementary features (rBRIEF) algorithm with rotation characteristics. Next, the bidirectional matching algorithm and nearest-neighbor distance ratio algorithm are used to implement feature point coarse matching; subsequently, the PROSAC algorithm is used to eliminate mismatches. Then, the global homography transformation model is used for image registration. Finally, the gradual-in and gradual-out image blending method is used to seamlessly blend the images. The experimental results indicate that the algorithm achieves excellent balance between accuracy and speed, and realizes fast and good image stitching.