Relying on landmarks for robust geolocation of drone and targets is one of the most important ways in GPS-denied environments. For small drones，there is no direct orientation capability without high-precision IMU. This paper presents an automated real-time matching and geolocation algorithm between video keyframes and landmark database based on the integration of visual SALM and YOLOv3 deep learning network method. The algorithm mainly extracts the landmarks from the drone video keyframe images to improve target geolocation accuracy, and designs different processing scheme of the keyframes which contains rich and spare landmarks. For feature extraction matching, we improved ORB feature extraction strategy, and obtained a more uniformly distributed feature points than original ORB feature extraction. In the three groups of top-down drone video images experiments, the 100 m, 200 m, and 300 m of the case were carried out to verify the robustness of the algorithm and being compared with GPS surveying data. The results show that the features of keyframe landmarks in the top-down video images within 300 m are stable to match the landmark database, the geolocation accuracy is controlled within 0.8 m, and it has good accuracy.
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