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.
基于3D高斯溅射（3DGS）的SLAM系统因其在实时高保真渲染中的优异表现而广受关注。然而，在包含动态物体的真实环境中，现有的基于3DGS的SLAM系统常常面临映射错误和跟踪漂移问题。为了解决这些问题，我们提出了GARAD-SLAM，一个专为动态场景量身定制的实时3DGS基SLAM系统。在跟踪方面，与传统方法不同，我们直接对高斯进行动态分割，并通过高斯金字塔网络将其映射回前端，通过高斯的动态标签实现精确的动态物体去除和稳健的跟踪。在映射方面，我们对动态标记的高斯施加渲染惩罚，这些高斯通过网络进行更新，从而避免了简单修剪导致的不可逆错误去除。我们在真实世界数据集上的结果表明，与基线方法相比，我们的方法在跟踪方面具有竞争力，渲染中生成的伪影较少，并且重建质量更高。