This paper presents a GPU-based real-time approach for generating high-definition (HD) aerial image mosaics. The cumbersome process of registering HD images is addressed by a parallel scheme that rapidly matches binary features. The proposed feature matcher takes advantage of the fast ORB (oriented FAST and rotated BRIEF) descriptor and its attainable arrangement into hash tables. By exploiting the best functionalities of binary descriptors and hashing-based data structures, the process of creating HD mosaics is accelerated. On average, real-time performance of 14.5 ms is achieved in a frame-to-frame process, for input images of 2.7 K resolution (2704 × 1521). For evaluation purposes in terms of robustness and speed, we selected two image registration methods for comparison. The first method uses the feature extractor and matcher modules of the well-known ORB-SLAM. The second comparison is carried out against the standard KNN-based matcher of OpenCV. The experiments were conducted under different conditions and scenarios, and the proposed approach exhibits a speed-up of 10.5 times compared to ORB-SLAM-based approach and 36.5 times compared to the OpenCV matcher. Therefore, this research widens the range of applications for aerial mosaicing, since the proposed system is capable of creating high-detail panoramas of large sites while acquiring data.

本文提出了一种基于GPU的实时方法，用于生成高清（HD）航拍图像镶嵌图。通过快速匹配二进制特征的并行方案解决了注册高清图像的麻烦过程。提出的特征匹配器利用了快速ORB（面向FAST和旋转后的Brief）描述符及其在哈希表中的可实现安排。通过利用二进制描述符和基于散列的数据结构的最佳功能，可以加快创建HD镶嵌的过程。对于2.7 K分辨率（2704×1521）的输入图像，在逐帧处理中平均可以实现14.5 ms的实时性能。为了评估鲁棒性和速度，我们选择了两种图像配准方法进行比较。第一种方法使用众所周知的ORB-SLAM的特征提取器和匹配器模块。与OpenCV的基于KNN的标准匹配器进行第二次比较。实验是在不同的条件和场景下进行的，与基于ORB-SLAM的方法相比，所提方法的速度提高了10.5倍，与OpenCV匹配器相比，速度提高了36.5倍。因此，这项研究拓宽了航空马赛克的应用范围，因为该系统能够在获取数据的同时创建大型站点的高细节全景图。与基于ORB-SLAM的方法相比，是5倍，与OpenCV匹配器相比，是36.5倍。因此，这项研究拓宽了航空马赛克的应用范围，因为该系统能够在获取数据的同时创建大型站点的高细节全景图。与基于ORB-SLAM的方法相比，是5倍，与OpenCV匹配器相比，是36.5倍。因此，这项研究拓宽了航空马赛克的应用范围，因为该系统能够在获取数据的同时创建大型站点的高细节全景图。