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.

翻译：近年来，神经辐射场（NeRF）被广泛用作稠密同时定位与地图构建（SLAM）的三维表示。尽管基于NeRF的方法在表面建模和新视角合成方面取得了显著成功，但其计算密集且耗时的体绘制管线限制了实际应用。本文提出了一种高效的稠密RGB-D SLAM系统，即CG-SLAM，它基于一种具有高一致性与几何稳定性的新型不确定性感知三维高斯场。通过对高斯泼溅的深入分析，我们提出了若干技术来构建适用于跟踪与建图的一致且稳定的三维高斯场。此外，我们提出了一种新的深度不确定性模型，以确保在优化过程中选择有价值的高斯基元，从而提升跟踪效率与精度。在多个数据集上的实验表明，CG-SLAM实现了卓越的跟踪与建图性能，跟踪速度高达15 Hz。我们将公开源代码。项目页面：https://zju3dv.github.io/cg-slam。