Aiming at the issue of poor stability of visual localization and mapping (SLAM) methods during dynamic low-altitude flight of unmanned aerial vehicles (UAVs) in the absence of navigation signals, this paper proposes a UAV visual localization method based on edge features, which generates the edge features by downsizing the traditional feature extraction algorithm and finally completes the position estimation by nonlinear optimization. A convolutional neural network is employed to match edge fea-tures between consecutive key frames, yielding an edge feature reprojection error function, and finally the position estimation is com-pleted by nonlinear optimization. The experimental results demonstrate that compared to the state-of-the-art ORB-SLAM3 algo-rithm, the proposed method reduces localization time by 31% on the dataset and improves localization accuracy by 15.04% in low-texture scenes. Flight experiments further indicate a significant enhancement in the accuracy and stability of UAV localization.