GARAD-SLAM：用于实时抗动态SLAM的3D Gaussian Splatting
GARAD-SLAM: 3D GAussian splatting for Real-time Anti Dynamic SLAM
AI总结：
针对动态场景下3DGS SLAM的跟踪漂移与建图误差，GARAD-SLAM利用高斯金字塔网络直接分割动态高斯并施加渲染惩罚，实现实时鲁棒跟踪与高质量重建。
摘要：
基于3D Gaussian Splatting（3DGS）的SLAM系统因其在实时高保真渲染方面的出色表现而受到广泛关注。然而，在包含动态物体的真实环境中，现有基于3DGS的SLAM系统往往面临建图误差和跟踪漂移问题。为解决这些问题，我们提出GARAD-SLAM，这是一种面向动态场景定制的、基于3DGS的实时SLAM系统。在跟踪方面，与传统方法不同，我们通过Gaussian pyramid network直接对Gaussians执行动态分割，并将其映射回前端以获得动态点标签，从而实现精确的动态移除和鲁棒跟踪。在建图方面，我们对经网络更新、被标记为动态的Gaussians施加渲染惩罚，以避免简单剪枝导致的不可逆错误移除。我们在真实世界数据集上的结果表明，与基线方法相比，本方法在跟踪方面具有竞争力，并在渲染中产生更少伪影和更高质量的重建。
英文摘要：
The 3D Gaussian Splatting (3DGS)-based SLAM system has garnered widespread attention due to its excellent performance in real-time high-fidelity rendering. However, in real-world environments with dynamic objects, existing 3DGS-based SLAM systems often face mapping errors and tracking drift issues. To address these problems, we propose GARAD-SLAM, a real-time 3DGS-based SLAM system tailored for dynamic scenes. In terms of tracking, unlike traditional methods, we directly perform dynamic segmentation on Gaussians and map them back to the front-end to obtain dynamic point labels through a Gaussian pyramid network, achieving precise dynamic removal and robust tracking. For mapping, we impose rendering penalties on dynamically labeled Gaussians, which are updated through the network, to avoid irreversible erroneous removal caused by simple pruning. Our results on real-world datasets demonstrate that our method is competitive in tracking compared to baseline methods, generating fewer artifacts and higher-quality reconstructions in rendering.
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