Real-time SLAM with dense 3D mapping is computationally challenging, especially on resource-limited devices. The recent development of 3D Gaussian Splatting (3DGS) offers a promising approach for real-time dense 3D reconstruction. However, existing 3DGS-based SLAM systems struggle to balance hardware simplicity, speed, and map quality. Most systems excel in one or two of the aforementioned aspects but rarely achieve all. A key issue is the difficulty of initializing 3D Gaussians while concurrently conducting SLAM. To address these challenges, we present Monocular GSO (MGSO), a novel real-time SLAM system that integrates photometric SLAM with 3DGS. Photometric SLAM provides dense structured point clouds for 3DGS initialization, accelerating optimization and producing more efficient maps with fewer Gaussians. As a result, experiments show that our system generates reconstructions with a balance of quality, memory efficiency, and speed that outperforms the state-of-the-art. Furthermore, our system achieves all results using RGB inputs. We evaluate the Replica, TUM-RGBD, and EuRoC datasets against current live dense reconstruction systems. Not only do we surpass contemporary systems, but experiments also show that we maintain our performance on laptop hardware, making it a practical solution for robotics, A/R, and other real-time applications.
实时SLAM与密集3D映射在计算上非常具有挑战性，尤其是在资源有限的设备上。近年来，3D Gaussian Splatting (3DGS) 的发展为实时密集3D重建提供了一种有前景的解决方案。然而，现有基于3DGS的SLAM系统在硬件简化、速度和地图质量之间难以平衡。大多数系统在上述某一或两方面表现优异，但很少能全面实现这一平衡。一个关键问题是在同时进行SLAM的过程中初始化3D高斯非常困难。为了解决这些挑战，我们提出了Monocular GSO (MGSO)，这是一种结合光度SLAM与3DGS的全新实时SLAM系统。光度SLAM为3DGS初始化提供了稠密的结构化点云，加速了优化过程，并以更少的高斯生成了更高效的地图。实验结果表明，我们的系统在质量、内存效率和速度之间实现了平衡，优于现有的最先进方法。此外，我们的系统仅使用RGB输入即可实现所有结果。我们在Replica、TUM-RGBD和EuRoC数据集上对比了当前的实时密集重建系统，实验不仅表明我们超越了现有系统，还展示了在笔记本硬件上保持性能的能力，使其成为机器人、增强现实（A/R）以及其他实时应用的实际解决方案。