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.
我们提出了动态高斯点 SLAM（DGS-SLAM），这是第一个基于高斯点绘制构建的动态 SLAM 框架。尽管最近稠密 SLAM 的进展已经利用高斯点绘制来增强场景表示，但大多数方法假设环境是静态的，这使其容易受到动态物体引起的光度和几何不一致的影响。为了解决这些问题，我们将高斯点 SLAM 与鲁棒过滤流程相结合，以处理整个管道中的动态物体，包括高斯点的插入和关键帧选择。 在该框架中，为了进一步提高动态物体移除的精度，我们引入了一种鲁棒的遮罩生成方法，通过在关键帧之间强制光度一致性，减少了由于不准确分割和诸如阴影等伪影引起的噪声。此外，我们提出了一种回环感知的窗口选择机制，利用 3D 高斯点的唯一关键帧 ID 检测当前帧与历史帧之间的回环，从而实现当前相机位姿和高斯地图的联合优化。 DGS-SLAM 在多个动态 SLAM 基准上，在相机追踪和新视图合成任务中都达到了最先进的性能，证明了其在处理真实动态场景方面的有效性。