Recent research on Simultaneous Localization and Mapping (SLAM) based on implicit representation has shown promising results in indoor environments. However, there are still some challenges: the limited scene representation capability of implicit encodings, the uncertainty in the rendering process from implicit representations, and the disruption of consistency by dynamic objects. To address these challenges, we propose a real-time dynamic visual SLAM system based on local-global fusion neural implicit representation, named DVN-SLAM. To improve the scene representation capability, we introduce a local-global fusion neural implicit representation that enables the construction of an implicit map while considering both global structure and local details. To tackle uncertainties arising from the rendering process, we design an information concentration loss for optimization, aiming to concentrate scene information on object surfaces. The proposed DVN-SLAM achieves competitive performance in localization and mapping across multiple datasets. More importantly, DVN-SLAM demonstrates robustness in dynamic scenes, a trait that sets it apart from other NeRF-based methods.

翻译：近期基于隐式表示的同时定位与地图构建（SLAM）研究在室内环境中展现出良好前景。然而仍存在若干挑战：隐式编码的场景表征能力有限、隐式表示渲染过程中的不确定性，以及动态物体对一致性的破坏。为解决上述问题，本文提出一种基于局部-全局融合神经隐式表示的实时动态视觉SLAM系统，命名为DVN-SLAM。为提升场景表征能力，我们引入局部-全局融合神经隐式表示，在构建隐式地图时兼顾全局结构与局部细节。针对渲染过程中的不确定性，我们设计了一种信息集中损失函数用于优化，旨在将场景信息聚焦于物体表面。所提出的DVN-SLAM在多个数据集上的定位与地图构建性能均达到竞争力水平。更重要的是，DVN-SLAM在动态场景中展现出鲁棒性，这一特性使其区别于其他基于NeRF的方法。