Robust visual SLAM based on heterogeneous feature data association

Visual SLAM (VSLAM) is a key technology for intelligent unmanned systems to achieve environmental perception and autonomous localization. In response to the issues of unstable feature extraction and the decreased adaptability and accuracy of SLAM systems caused by dynamic illumination changes in practical application scenarios, this paper proposes a robust VSLAM system based on heterogeneous feature data association, which does not require pre-trained models and can operate stably on resource-constrained platforms. The system designs a boundary-aware adaptive feature detection framework that maintains a stable number of features under complex illumination conditions through mirror padding and dynamic threshold adjustment. It also incorporates an edge-feature-guided quadtree optimization mechanism, constructing a region of interest (ROI) spatial mask to guide feature distribution and improve feature repeatability. By combining the GMS algorithm to enhance matching robustness, high-quality feature points are provided for back-end pose estimation, comprehensively improving the accuracy and adaptability of the SLAM system. Experimental results show that the proposed method achieves more stable and abundant feature extraction under complex illumination conditions. Compared with ORB-SLAM2, the feature repeatability rate is improved by approximately 39.8%, and the RMSE in multiple indoor and outdoor scenarios is reduced by 21.9%, providing an effective solution for the stable deployment and reliable operation of SLAM in practical applications.