3D Gaussian Splatting algorithms excel in novel view rendering applications and have been adapted to extend the capabilities of traditional SLAM systems. However, current Gaussian Splatting SLAM methods, designed mainly for hand-held RGB or RGB-D sensors, struggle with tracking drifts when used with rotating RGB-D camera setups. In this paper, we propose a robust Gaussian Splatting SLAM architecture that utilizes inputs from rotating multiple RGB-D cameras to achieve accurate localization and photorealistic rendering performance. The carefully designed Gaussian Splatting Loop Closure module effectively addresses the issue of accumulated tracking and mapping errors found in conventional Gaussian Splatting SLAM systems. First, each Gaussian is associated with an anchor frame and categorized as historical or novel based on its timestamp. By rendering different types of Gaussians at the same viewpoint, the proposed loop detection strategy considers both co-visibility relationships and distinct rendering outcomes. Furthermore, a loop closure optimization approach is proposed to remove camera pose drift and maintain the high quality of 3D Gaussian models. The approach uses a lightweight pose graph optimization algorithm to correct pose drift and updates Gaussians based on the optimized poses. Additionally, a bundle adjustment scheme further refines camera poses using photometric and geometric constraints, ultimately enhancing the global consistency of scenarios. Quantitative and qualitative evaluations on both synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art methods in camera pose estimation and novel view rendering tasks. The code will be open-sourced for the community.
3D 高斯分布（3D Gaussian Splatting）算法在新视角渲染应用中表现出色，并且已被扩展应用于传统 SLAM 系统。然而，当前的高斯分布 SLAM 方法主要针对手持 RGB 或 RGB-D 传感器设计，在旋转 RGB-D 摄像机设置下容易出现跟踪漂移问题。为了解决这一问题，我们提出了一种鲁棒的高斯分布 SLAM 架构，利用多个旋转 RGB-D 摄像机的输入，实现准确的定位和逼真的渲染性能。我们精心设计的高斯分布闭环检测模块有效解决了常规高斯分布 SLAM 系统中累积的跟踪和建图误差问题。首先，我们为每个高斯关联一个锚定帧，并根据其时间戳将其分类为历史高斯或新高斯。通过在同一视点渲染不同类型的高斯，所提出的闭环检测策略同时考虑了可视性关系和不同的渲染效果。此外，我们提出了一种闭环优化方法，用于消除相机姿态漂移并保持三维高斯模型的高质量。该方法使用轻量级的姿态图优化算法来校正姿态漂移，并根据优化后的姿态更新高斯。此外，捆绑调整方案进一步通过光度和几何约束优化相机姿态，最终增强场景的全局一致性。在合成数据集和真实数据集上的定量和定性评估表明，我们的方法在相机姿态估计和新视角渲染任务中优于最新的现有方法。