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.
最近，神经辐射场（NeRF）作为3D表示，已被广泛用于密集的同时定位与地图构建（SLAM）。尽管在表面建模和新视角合成方面取得了显著成功，但现有基于NeRF的方法受到其计算密集和耗时的体积渲染流程的阻碍。本文提出了一个高效的密集RGB-D SLAM系统，即CG-SLAM，基于一个新颖的、具有高一致性和几何稳定性的不确定性感知3D高斯场。通过对高斯喷溅的深入分析，我们提出了几种技术，以构建一个适用于跟踪和映射的一致且稳定的3D高斯场。此外，我们提出了一个新颖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原语，从而提高跟踪效率和准确性。在各种数据集上的实验表明，CG-SLAM实现了卓越的跟踪和映射性能，具有高达15 Hz的显著跟踪速度。