Combining 3D Gaussian splatting with Simultaneous Localization and Mapping (SLAM) has gained popularity as it enables continuous 3D environment reconstruction during motion. However, existing methods struggle in dynamic environments, particularly moving objects complicate 3D reconstruction and, in turn, hinder reliable tracking. The emergence of 4D reconstruction, especially 4D Gaussian splatting, offers a promising direction for addressing these challenges, yet its potential for 4D-aware SLAM remains largely underexplored. Along this direction, we propose a robust and efficient framework, namely Reweighting Uncertainty in Gaussian Splatting SLAM (RU4D-SLAM) for 4D scene reconstruction, that introduces temporal factors into spatial 3D representation while incorporating uncertainty-aware perception of scene changes, blurred image synthesis, and dynamic scene reconstruction. We enhance dynamic scene representation by integrating motion blur rendering, and improve uncertainty-aware tracking by extending per-pixel uncertainty modeling, which is originally designed for static scenarios, to handle blurred images. Furthermore, we propose a semantic-guided reweighting mechanism for per-pixel uncertainty estimation in dynamic scenes, and introduce a learnable opacity weight to support adaptive 4D mapping. Extensive experiments on standard benchmarks demonstrate that our method substantially outperforms state-of-the-art approaches in both trajectory accuracy and 4D scene reconstruction, particularly in dynamic environments with moving objects and low-quality inputs. Code available: https://ru4d-slam.github.io

翻译：[translated abstract in Chinese] 将三维高斯溅射与同时定位与地图构建（SLAM）相结合，因其能够在运动过程中实现连续的三维环境重建而受到广泛关注。然而，现有方法在动态环境中面临挑战，尤其是运动物体使三维重建复杂化，进而阻碍了可靠的跟踪。四维重建技术（特别是四维高斯溅射）的出现为解决这些问题提供了有前景的方向，但其在四维感知SLAM中的潜力尚未得到充分挖掘。沿此方向，我们提出了一种鲁棒且高效的框架——面向4D场景重建的高斯溅射SLAM不确定性重加权方法（RU4D-SLAM），该方法在空间三维表示中引入时间因子，同时融合了场景变化的不确定性感知、模糊图像合成以及动态场景重建。我们通过集成运动模糊渲染来增强动态场景表示，并通过将原本针对静态场景设计的逐像素不确定性建模扩展至模糊图像处理，提升了不确定性感知跟踪的性能。此外，我们提出了一种用于动态场景逐像素不确定性估计的语义引导重加权机制，并引入可学习的透明度权重以支持自适应四维建图。在标准基准上的大量实验表明，我们的方法在轨迹精度和四维场景重建方面均显著优于现有先进方法，尤其在包含运动物体和低质量输入的动态环境中表现突出。代码地址：https://ru4d-slam.github.io