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.

基于局部 - 全局融合神经隐式表示的实时动态可视化 SLAM 系统 (DVN-SLAM) 能够提高场景表示能力、处理渲染过程中的不确定性，并在多个数据集上实现了竞争性的定位和映射性能，在动态场景中表现出鲁棒性，这是与其他基于 NeRF 的方法不同之处。