Visual Self-Positioning of Low-Altitude Urban UAV Based on Improved Transformer Architecture

With the development of the Internet of Things, cross-view geolocation of satellite images and Unmanned Aerial Vehicles (UAVs) aerial images is gaining attention due to its significant potential in denied-navigation environments under Global Navigation Satellite System (GNSS). However, inadequate feature representation between views, as well as uncertainties in positional offsets and distance scales, constitute key challenges in this field. Existing research primarily focuses on extracting comprehensive and fine-grained feature information. However, effective feature representation and alignment are equally critical. Therefore, a novel visual localization algorithm based on Multi-scale Feature Fusion on improved Transformer architecture (MFFT) is proposed. The algorithm combines Adaptive semantic Feature extraction based on Improved ASPP (AFIA) model and Spatial Pyramid based on Multi-scale Subsampling (SPMS) model, and has strong robustness. MFFT cooperatively uses AFIA and SPMS to successfully achieve an effective balance between feature representation and alignment. Experimental results show that, on the common benchmark data set DenseUAV, compared with the most advanced algorithms in the same field, the R@1 of MFFT algorithm is improved by at least 2.23% and the SDM@1 is improved by at least 1.64%, thus achieving the most advanced cross-view matching performance, verifying that the proposed algorithm has strong competitiveness in UAV visual self-localization tasks, and opening up a new technical path for future UAV autonomous navigation and diversified mission execution in complex environments.