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.
3D Gaussian Splatting 最近作为 SLAM 系统中的一种替代场景表示方法显示了良好的前景，相较于神经隐式表示方法。然而，目前的方法要么缺乏密集深度图来监督映射过程，要么缺乏考虑环境规模的详细训练设计。为了解决这些缺陷，我们提出了 IG-SLAM，这是一个密集 RGB-only SLAM 系统，采用强健的 Dense-SLAM 方法进行跟踪，并将其与 Gaussian Splatting 相结合。通过跟踪提供的准确姿态和密集深度，构建环境的 3D 地图。此外，我们利用深度不确定性进行地图优化，以改进 3D 重建。我们在地图优化中的衰减策略提高了收敛速度，使系统能够以每秒 10 帧的速度运行。我们展示了与最先进的 RGB-only SLAM 系统相竞争的性能，同时实现了更快的操作速度。我们在 Replica、TUM-RGBD、ScanNet 和 EuRoC 数据集上进行了实验。该系统在大规模序列中实现了逼真的 3D 重建，特别是在 EuRoC 数据集上。