Abstract:
In this research, we aspire to utilize the acquisition method as it becomes more flexible
than previous and produce high-resolution images for navigating and localizing un manned aerial vehicles (UAVs). Unmanned Aerial Vehicles (UAVs), also known as aerial
drones, are pervasive in enormous fields, including photography, aerial mapping, agri culture, surveillance, search and rescue, and parcel delivery. However, drone and UAV
localization with cross-view matching technology is a challenging research topic. We fa cilitate navigation without the Global Positioning System (GPS), which is favourable in
GPS-denied environments. Moreover, it reinforces existing GPS-based systems. In this
hypothetical model, a high-resolution satellite map is initially made in accordance with
anticipated drone flying by using google map API. To acquire an attention-weighted
matrix, the SSC (self-self-cross) and SCC (self-cross-cross) attention combinations are
applied on drone and satellite images. we also developed adaptive clustering-based meth ods on a region that receives higher attention for improving the precision of matching
anchors and deleting outlier points. Our methods successfully identify relevant matches
in unstructured, regular patterns, texture-free, and virgin environments, which typically
provide an advantage in existing art matching algorithms. We evaluate the suggested
model’s qualitative and quantitative on data that acquired during various drone flights
in different regions.. .
