RoGER-SLAM: A Robust Gaussian Splatting SLAM System for Noisy and Low-light Environment Resilience

TL;DR

RoGER-SLAM is a novel 3D Gaussian Splatting SLAM system designed to maintain high mapping and tracking accuracy in noisy and low-light environments by integrating robust fusion, adaptive tracking, and CLIP-based enhancements.

Contribution

It introduces a robust SLAM framework that combines structure-preserving fusion, adaptive residual balancing, and CLIP-based semantic enhancement to improve performance under challenging conditions.

Findings

Significantly improves trajectory accuracy in noisy environments.

Enhances reconstruction quality under low-light conditions.

Outperforms existing 3DGS-SLAM systems in adverse environments.

Abstract

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.