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.

翻译：我们提出DDN-SLAM，一种专为动态场景设计的实时稠密神经隐式语义SLAM系统。现有神经隐式SLAM系统在静态场景中表现良好，但常因现实环境中动态干扰的挑战而导致跟踪与建图失效。DDN-SLAM利用深度语义系统提供的先验信息，结合条件概率场进行分割。通过构建深度引导的静态掩码并采用联合多分辨率哈希编码，在抑制动态信息干扰的同时，实现快速孔洞填充与高质量建图。为增强跟踪鲁棒性，我们利用经光流与关键帧验证的稀疏特征点，实现闭环检测与全局束调整优化。此外，DDN-SLAM支持单目、双目及RGB-D输入，可在20-30Hz频率下稳定运行。在6个虚拟/真实数据集上的广泛实验表明，本方法在动态与静态场景中均优于现有最先进方案。