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.

DDN-SLAM 是一个实时的密集神经隐式语义 SLAM 系统，旨在解决动态场景下的跟踪和建图挑战，通过利用深度语义系统提供的先验知识、结合条件概率场进行分割，并采用深度引导静态遮罩和联合多分辨率哈希编码以提高建图质量。同时采用稀疏特征点和光流验证的方法增强跟踪鲁棒性，实现闭环检测和全局束优化。实验证明，在动态和静态场景中，DDN-SLAM 方法优于当前最先进方法。