Achieving robust and precise pose estimation in dynamic scenes is a significant research challenge in Visual Simultaneous Localization and Mapping (SLAM). Recent advancements integrating Gaussian Splatting into SLAM systems have proven effective in creating high-quality renderings using explicit 3D Gaussian models, significantly improving environmental reconstruction fidelity. However, these approaches depend on a static environment assumption and face challenges in dynamic environments due to inconsistent observations of geometry and photometry. To address this problem, we propose DG-SLAM, the first robust dynamic visual SLAM system grounded in 3D Gaussians, which provides precise camera pose estimation alongside high-fidelity reconstructions. Specifically, we propose effective strategies, including motion mask generation, adaptive Gaussian point management, and a hybrid camera tracking algorithm to improve the accuracy and robustness of pose estimation. Extensive experiments demonstrate that DG-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, and novel-view synthesis in dynamic scenes, outperforming existing methods meanwhile preserving real-time rendering ability.
在动态场景中实现鲁棒且精确的姿态估计是视觉同时定位与建图（Visual SLAM）领域的一个重要研究难题。最近，结合 3D Gaussian Splatting 的 SLAM 系统在通过显式 3D 高斯模型生成高质量渲染方面表现出色，显著提高了环境重建的保真度。然而，这些方法依赖静态环境假设，在动态场景中，由于几何和光度观测的不一致性而面临挑战。 为了解决这一问题，我们提出了 DG-SLAM，这是首个基于 3D 高斯的鲁棒动态视觉 SLAM 系统，同时提供精确的相机姿态估计和高保真的重建。具体而言，我们设计了多项有效策略，包括运动掩膜生成、适应性高斯点管理以及混合相机跟踪算法，从而提升姿态估计的准确性和鲁棒性。 大量实验表明，DG-SLAM 在动态场景中的相机姿态估计、地图重建以及新视图合成方面实现了最先进的性能，同时保留了实时渲染能力，显著优于现有方法。