Simultaneous localization and mapping (SLAM) technology has recently achieved photorealistic mapping capabilities thanks to the real-time, high-fidelity rendering enabled by 3D Gaussian Splatting (3DGS). However, due to the static representation of scenes, current 3DGS-based SLAM encounters issues with pose drift and failure to reconstruct accurate maps in dynamic environments. To address this problem, we present D4DGS-SLAM, the first SLAM method based on 4DGS map representation for dynamic environments. By incorporating the temporal dimension into scene representation, D4DGS-SLAM enables high-quality reconstruction of dynamic scenes. Utilizing the dynamics-aware InfoModule, we can obtain the dynamics, visibility, and reliability of scene points, and filter out unstable dynamic points for tracking accordingly. When optimizing Gaussian points, we apply different isotropic regularization terms to Gaussians with varying dynamic characteristics. Experimental results on real-world dynamic scene datasets demonstrate that our method outperforms state-of-the-art approaches in both camera pose tracking and map quality.

翻译：同步定位与建图（SLAM）技术近期因3D高斯溅射（3DGS）所实现的实时、高保真渲染能力，已获得照片级真实感的建图性能。然而，由于其对场景的静态表示，当前基于3DGS的SLAM在动态环境中面临位姿漂移及无法重建精确地图的问题。为解决此问题，我们提出了D4DGS-SLAM，首个面向动态环境、基于4DGS地图表示的SLAM方法。通过将时间维度纳入场景表示，D4DGS-SLAM能够实现动态场景的高质量重建。利用动态感知信息模块（InfoModule），我们可以获取场景点的动态性、可见性与可靠性，并据此滤除不稳定的动态点以进行跟踪。在优化高斯点时，我们对具有不同动态特性的高斯点应用不同的各向同性正则化项。在真实世界动态场景数据集上的实验结果表明，我们的方法在相机位姿跟踪与地图质量方面均优于现有最先进方法。