Recently neural radiance fields (NeRF) have been widely exploited as 3D
representations for dense simultaneous localization and mapping (SLAM). Despite
their notable successes in surface modeling and novel view synthesis, existing
NeRF-based methods are hindered by their computationally intensive and
time-consuming volume rendering pipeline. This paper presents an efficient
dense RGB-D SLAM system, i.e., CG-SLAM, based on a novel uncertainty-aware 3D
Gaussian field with high consistency and geometric stability. Through an
in-depth analysis of Gaussian Splatting, we propose several techniques to
construct a consistent and stable 3D Gaussian field suitable for tracking and
mapping. Additionally, a novel depth uncertainty model is proposed to ensure
the selection of valuable Gaussian primitives during optimization, thereby
improving tracking efficiency and accuracy. Experiments on various datasets
demonstrate that CG-SLAM achieves superior tracking and mapping performance
with a notable tracking speed of up to 15 Hz. We will make our source code
publicly available. Project page: this https URL

基于新颖的具备高一致性和几何稳定性的不确定性感知的 3D 高斯场，这篇论文提出了一种有效的 RGB-D SLAM 系统，CG-SLAM，在跟踪和地图制作方面实现了卓越的性能。