首个基于高斯点阵的动态环境SLAM，能有效处理移动物体干扰。
DGS-SLAM: Gaussian Splatting SLAM in Dynamic Environment
- 将高斯点阵与鲁棒滤波结合，全程处理动态物体。
- 在多个动态场景基准上实现最佳跟踪与新视角合成效果。
- 适合需要精准动态环境建模的自动驾驶与机器人应用。
我们提出动态高斯点阵SLAM（DGS-SLAM），首个基于高斯点阵的动态环境SLAM框架。尽管近期密集SLAM利用高斯点阵提升场景表示，但多数方法假设环境静态，易受动态物体引起的光度和几何不一致影响。为此，我们将高斯点阵SLAM与鲁棒滤波过程融合，贯穿高斯插入与关键帧选择全流程以处理动态物体。为提升动态物体移除精度，引入一种鲁棒掩码生成方法，强制跨关键帧保持光度一致性，降低误分割噪声与阴影等伪影。此外，提出环路感知窗口选择机制，利用3D高斯的独特关键帧ID检测当前与历史帧间的环路，支持相机位姿与高斯地图联合优化。DGS-SLAM在多个动态SLAM基准上实现最优的相机追踪与新视角合成性能，验证了其在真实动态场景中的有效性。
原文摘要 · Abstract (English)
We introduce Dynamic Gaussian Splatting SLAM (DGS-SLAM), the first dynamic SLAM framework built on the foundation of Gaussian Splatting. While recent advancements in dense SLAM have leveraged Gaussian Splatting to enhance scene representation, most approaches assume a static environment, making them vulnerable to photometric and geometric inconsistencies caused by dynamic objects. To address these challenges, we integrate Gaussian Splatting SLAM with a robust filtering process to handle dynamic objects throughout the entire pipeline, including Gaussian insertion and keyframe selection. Within this framework, to further improve the accuracy of dynamic object removal, we introduce a robust mask generation method that enforces photometric consistency across keyframes, reducing noise from inaccurate segmentation and artifacts such as shadows. Additionally, we propose the loop-aware window selection mechanism, which utilizes unique keyframe IDs of 3D Gaussians to detect loops between the current and past frames, facilitating joint optimization of the current camera poses and the Gaussian map. DGS-SLAM achieves state-of-the-art performance in both camera tracking and novel view synthesis on various dynamic SLAM benchmarks, proving its effectiveness in handling real-world dynamic scenes.
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