传统视觉特征匹配算法提取的特征点易受光照、视角变化等外界因素的影响，复杂环境下算法匹配精度低、鲁棒性差，导致无人机视觉定位误差大。针对上述问题，提出了一种融合多类信息的语义特征匹配算法，该算法采用语义特征点替代传统特征点以提高特征点的鲁棒性与可重复提取率。首先，采用位置信息、语义信息以及描述信息建立特征点的初始表征；随后，利用具有注意力机制的图神经网络聚合其他语义特征点信息，建立最终表征，以优化表征结果的精确性；最后，根据语义信息与表征信息计算特征点对的综合匹配度，提高特征匹配的准确率。实验结果表明：在无人机飞行场景下，所提算法特征点的稳健性、特征匹配准确性均优于传统算法，且对光照、大视角等变化具有较强的鲁棒性，为后端无人机视觉导航位姿解算提供了更加可靠精准的语义特征匹配结果。

The feature points extracted by traditional methods in UAV navigation are susceptible to external factors such as changes in lighting and viewing angles， and the low matching accuracy and poor robustness of the algorithm in complex environment lead to large errors in pose estimation. To address these issues， this paper proposes a semantic feature matching algorithm that integrates multiple types of information. The algorithm replaces traditional feature points with semantic feature points to enhance robustness and repeatability of point extraction. To optimize the accuracy of the description， we establish the initial representations of feature points via positional， semantic， and descriptive information. Subsequently， a graph neural network incorporating a fusion attention mechanism is used to aggregate the information from other semantic feature points， so as to create the final representation. Finally， the comprehensive matching degree of feature point pairs is calculated based on semantic and characterization information to enhance the accuracy of feature matching. Experimental results demonstrate that in UAV flight scenarios， the proposed algorithm exhibits better robustness and feature matching accuracy compared to traditional algorithms. Moreover， it shows strong robustness to changes in lighting and large viewing angles， providing more reliable and accurate landmark matching for back-end UAV visual pose estimation.