DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM with Joint Semantic Encoding

We propose DDN-SLAM, a real-time dense neural implicit semantic SLAM system designed for dynamic scenes. While existing neural implicit SLAM systems perform well in static scenes, they often encounter challenges in real-world environments with dynamic interferences, leading to ineffective tracking and mapping. DDN-SLAM utilizes the priors provided by the deep semantic system, combined with conditional probability fields, for segmentation.By constructing depth-guided static masks and employing joint multi-resolution hashing encoding, we ensure fast hole filling and high-quality mapping while mitigating the effects of dynamic information interference. To enhance tracking robustness, we utilize sparse feature points validated with optical flow and keyframes, enabling loop closure detection and global bundle optimization. Furthermore, DDN-SLAM supports monocular, stereo, and RGB-D inputs, operating robustly at a frequency of 20-30Hz. Extensive experiments on 6 virtual/real datasets demonstrate that our method outperforms state-of-the-art approaches in both dynamic and static scenes.