3D Gaussian Splatting has recently shown promising results as an alternative scene representation in SLAM systems to neural implicit representations. However, current methods either lack dense depth maps to supervise the mapping process or detailed training designs that consider the scale of the environment. To address these drawbacks, we present IG-SLAM, a dense RGB-only SLAM system that employs robust Dense-SLAM methods for tracking and combines them with Gaussian Splatting. A 3D map of the environment is constructed using accurate pose and dense depth provided by tracking. Additionally, we utilize depth uncertainty in map optimization to improve 3D reconstruction. Our decay strategy in map optimization enhances convergence and allows the system to run at 10 fps in a single process. We demonstrate competitive performance with state-of-the-art RGB-only SLAM systems while achieving faster operation speeds. We present our experiments on the Replica, TUM-RGBD, ScanNet, and EuRoC datasets. The system achieves photo-realistic 3D reconstruction in large-scale sequences, particularly in the EuRoC dataset.

本研究解决了现有SLAM系统在映射过程中缺乏密集深度图和大规模环境训练设计的问题。提出的IG-SLAM是一种仅依赖RGB图像的密集SLAM系统，通过有效结合Dense-SLAM方法与高斯散点技术，构建了准确的3D环境地图，并优化了3D重建过程。实验结果表明，该系统在多个数据集上与最新的RGB SLAM系统表现出竞争力，同时实现了更快的操作速度。

IG-SLAM：即时高斯SLAM