景象匹配无人机视觉定位

Drones play an important role in vicinagearth security, post-disaster rescue, geological survey, agricultural plant protection, and other fields due to their high flexibility, and they receive increasing attention. As a key technology in drones, positioning and navigation are crucial for whether the drone can successfully perform tasks. Currently, the main positioning and navigation algorithms include the global navigation satellite system, inertial positioning, and scene matching positioning and navigation. Among them, the scene matching positioning and navigation method uses computer vision technology to encode the digital features of aerial images collected during the flight of drones. Then, by constructing a similarity measurement and retrieval model, it measures the similarity between the aerial image features and the pre-obtained remote sensing map library features to complete the scene matching. Finally, based on the matching results of drone aerial images and remote sensing satellite maps, it obtains the corresponding geographic position information and updates it as the positioning result of the drone. The scene matching positioning and navigation method eliminates the dependence of the positioning system on positioning signals and realizes the autonomy of drone flight positioning. This paper follows the feature extraction methods in the scene matching algorithm and outlines the development process of scene matching based on template matching, manual feature-based, and metric learning-based approaches while summarizing the key problems in the positioning and navigation methods of scene matching. Finally, this paper summarizes the urgent problems that need to be solved in drone scene matching localization methods based on the current development status of scene matching algorithms.