CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D
  Gaussian Field
Jiarui Hu Xianhao Chen Boyin Feng Guanglin Li Liangjing Yang Hujun Bao Guofeng Zhang Zhaopeng Cui
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: https://zju3dv.github.io/cg-slam.