2HR-Net VSLAM: Robust visual SLAM based on dual high-reliability feature matching in dynamic environments

Visual Simultaneous Localization and Mapping (VSLAM) is the key technology for autonomous navigation of mobile robots. However, feature-based VSLAM systems still face two major challenges in dynamic complex environments: insufficient feature reliability and significant dynamic interference, urgently requiring improved matching robustness. This paper innovatively proposes a dynamic adaptive VSLAM system based on the High-repeatability and High-reliability feature matching network (2HR-Net), which improves localization accuracy in dynamic environments through three key innovations: First, the 2HR feature detection network is designed, integrating the K-Means clustering algorithm into L2-Net to achieve feature point detection with both high repeatability and high reliability. Second, the lightweight YOLOv8n model is integrated to detect and remove feature points in dynamic regions in real-time, effectively reducing the impact of dynamic interference on pose estimation. Finally, the shared matching Siamese network with a unique dual-branch feature fusion strategy and similarity optimization algorithm is proposed to enhance the accuracy of feature matching. The proposed algorithm was ultimately validated using the publicly available TUM dataset. The experimental results show that the feature detection method proposed in this paper achieved a repeatability rate of approximately 70% in various dynamic scenarios, which is significantly higher than traditional methods (such as ORB-SLAM3), whose repeatability typically falls below 40%. In addition, compared with ORB-SLAM3, the root mean square error (RMSE) and standard deviation (S.D.) of the Absolute Trajectory Error (ATE) in various dynamic scenarios were reduced by approximately 90%, indicating higher localization accuracy and stability. Therefore, the experimental results demonstrate that the proposed method outperforms mainstream methods such as ORB-SLAM3 in terms of feature repeatability, matching accuracy, and localization precision, providing an effective solution for robust VSLAM in dynamic environments.