Deep-UAV SLAM: SuperPoint and SuperGlue enhanced SLAM for dynamic outdoor air navigation

Abstract. Combining traditional Simultaneous Localization and Mapping(SLAM) with deep learning techniques leverages the strengths of machine learning in feature extraction and matching, thereby enhancing SLAM performance in UAV-based aerial RGB imagery scenarios. The core contribution of this study lies in upgrading the front-end of ORB-SLAM3 by adopting deep learning-based features (SuperPoint) and a matcher (SuperGlue), thereby replacing its original ORB feature extraction and matching modules. Experimental results demonstrate that, compared to classical handcrafted features, deep learning-based feature matching achieves higher robustness and accuracy in UAV SLAM tasks. Overall, the proposed method outperforms traditional SLAM approaches in both accuracy and robustness.