The reliability of Simultaneous Localization and Mapping (SLAM) is severely constrained in environments where visual inputs suffer from noise and low illumination. Although recent 3D Gaussian Splatting (3DGS) based SLAM frameworks achieve high-fidelity mapping under clean conditions, they remain vulnerable to compounded degradations that degrade mapping and tracking performance. A key observation underlying our work is that the original 3DGS rendering pipeline inherently behaves as an implicit low-pass filter, attenuating high-frequency noise but also risking over-smoothing. Building on this insight, we propose RoGER-SLAM, a robust 3DGS SLAM system tailored for noise and low-light resilience. The framework integrates three innovations: a Structure-Preserving Robust Fusion (SP-RoFusion) mechanism that couples rendered appearance, depth, and edge cues; an adaptive tracking objective with residual balancing regularization; and a Contrastive Language-Image Pretraining (CLIP)-based enhancement module, selectively activated under compounded degradations to restore semantic and structural fidelity. Comprehensive experiments on Replica, TUM, and real-world sequences show that RoGER-SLAM consistently improves trajectory accuracy and reconstruction quality compared with other 3DGS-SLAM systems, especially under adverse imaging conditions.

翻译：在视觉输入受噪声与低光照影响的环境中，同步定位与建图（SLAM）的可靠性受到严重制约。尽管近期基于三维高斯泼溅（3DGS）的SLAM框架在洁净条件下实现了高保真建图，但其仍易受复合退化影响，导致建图与跟踪性能下降。本研究基于一个关键观察：原始3DGS渲染流程本质上表现为一种隐式低通滤波器，在抑制高频噪声的同时亦存在过度平滑的风险。基于此洞见，我们提出RoGER-SLAM——一种专为噪声与低光环境鲁棒性设计的稳健3DGS SLAM系统。该框架融合三项创新：耦合渲染外观、深度与边缘线索的结构保持鲁棒融合（SP-RoFusion）机制；配备残差平衡正则化的自适应跟踪目标函数；以及基于对比语言-图像预训练（CLIP）的增强模块，该模块在复合退化条件下选择性激活以恢复语义与结构保真度。在Replica、TUM及真实场景序列上的综合实验表明，相较于其他3DGS-SLAM系统，RoGER-SLAM在轨迹精度与重建质量方面均取得持续提升，尤其在恶劣成像条件下表现显著。