This study aimed to achieve the accurate and real-time geographic positioning of UAV aerial image targets. We verified a method of registering UAV camera images on a map (with the geographic location) through feature matching. The UAV is usually in rapid motion and involves changes in the camera head, and the map is high-resolution and has sparse features. These reasons make it difficult for the current feature-matching algorithm to accurately register the two (camera image and map) in real time, meaning that there will be a large number of mismatches. To solve this problem, we used the SuperGlue algorithm, which has a better performance, to match the features. The layer and block strategy, combined with the prior data of the UAV, was introduced to improve the accuracy and speed of feature matching, and the matching information obtained between frames was introduced to solve the problem of uneven registration. Here, we propose the concept of updating map features with UAV image features to enhance the robustness and applicability of UAV aerial image and map registration. After numerous experiments, it was proved that the proposed method is feasible and can adapt to the changes in the camera head, environment, etc. The UAV aerial image is stably and accurately registered on the map, and the frame rate reaches 12 frames per second, which provides a basis for the geo-positioning of UAV aerial image targets.
Keywords: SuperGlue; drone; feature matching; image blocking; real-time image registration; target geolocation.