Abstract
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.
Abstract (translated)
基于隐式表示的同步定位与映射（SLAM）研究在室内环境中取得了良好的结果。然而，仍然存在一些挑战：隐式编码的有限场景表示能力，从隐式表示中渲染过程的不确定性以及动态物体对一致性的干扰。为了应对这些挑战，我们提出了一个基于局部-全局融合神经隐式表示的实时动态SLAM系统，名为DVN-SLAM。为了提高场景表示能力，我们引入了一种局部-全局融合神经隐式表示，使得在考虑全局结构和局部细节的同时构建隐含地图。为了应对渲染过程产生的不确定性，我们设计了一个信息聚类损失，旨在将场景信息集中在物体表面。所提出的DVN-SLAM在多个数据集上的定位和映射达到竞争力的性能。更重要的是，DVN-SLAM展示了在动态场景中的鲁棒性，这是其他基于NeRF的方法所不具备的。
URL
https://arxiv.org/abs/2403.11776
