High-Precision Visual SLAM for Dynamic Scenes Using Semantic–Geometric Feature Filtering and NeRF Maps

Dynamic environments pose significant challenges for visual SLAM, including feature ambiguity, weak textures, and map inconsistencies caused by moving objects. We present a robust SLAM framework integrating image enhancement, a mixed-precision quantized feature detection network, semantic-driven dynamic feature filtering, and NeRF-based static scene reconstruction. The system reliably extracts features under challenging conditions, removes dynamic points using instance segmentation combined with polar geometric constraints, and reconstructs static scenes with enhanced structural fidelity. Extensive experiments on TUM RGB-D, BONN RGB-D, and a custom dataset demonstrate notable improvements in the RMSE, mean, median, and standard deviation. Compared with ORB-SLAM3, our method achieves an average RMSE reduction of 93.4%, demonstrating substantial improvement, and relative to other state-of-the-art dynamic SLAM systems, it improves the average RMSE by 49.6% on TUM and 23.1% on BONN, highlighting its high accuracy, robustness, and adaptability in complex and highly dynamic environments.