RoGER-SLAM：面向噪声和低光照环境鲁棒性的高斯溅射SLAM系统
RoGER-SLAM: A Robust Gaussian Splatting SLAM System for Noisy and Low-light Environment Resilience
AI总结：
针对噪声和低光照环境，提出RoGER-SLAM，通过结构保持鲁棒融合、自适应跟踪和CLIP增强模块，提升3DGS-SLAM的轨迹精度和重建质量。
摘要：
同时定位与地图构建（SLAM）的可靠性在视觉输入受到噪声和低光照影响的环境中受到严重制约。尽管基于三维高斯溅射（3DGS）的SLAM框架在清洁条件下实现了高保真建图，但它们仍然容易受到复合退化影响，从而降低建图和跟踪性能。我们工作的一个关键观察是，原始3DGS渲染管线本质上表现为隐式低通滤波器，衰减高频噪声但也存在过度平滑的风险。基于这一洞察，我们提出了RoGER-SLAM，一个针对噪声和低光照鲁棒性定制的稳健3DGS SLAM系统。该框架整合了三个创新：结构保持鲁棒融合（SP-RoFusion）机制，耦合渲染外观、深度和边缘线索；具有残差平衡正则化的自适应跟踪目标；以及基于对比语言-图像预训练（CLIP）的增强模块，在复合退化条件下选择性激活以恢复语义和结构保真度。在Replica、TUM和真实世界序列上的全面实验表明，与其他3DGS-SLAM系统相比，RoGER-SLAM持续提高了轨迹精度和重建质量，尤其是在不利成像条件下。
英文摘要：
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.
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