Thermal imaging provides a practical sensing modality for visual SLAM in visually degraded environments such as low illumination, smoke, or adverse weather. However, thermal imagery often exhibits low texture, low contrast, and high noise, complicating feature-based SLAM. In this work, we propose a sparse monocular graph-based SLAM system for thermal imagery that leverages general-purpose learned features -- the SuperPoint detector and LightGlue matcher, trained on large-scale visible-spectrum data to improve cross-domain generalization. To adapt these components to thermal data, we introduce a preprocessing pipeline to enhance input suitability and modify core SLAM modules to handle sparse and outlier-prone feature matches. We further incorporate keypoint confidence scores from SuperPoint into a confidence-weighted factor graph to improve estimation robustness. Evaluations on public thermal datasets demonstrate that the proposed system achieves reliable performance without requiring dataset-specific training or fine-tuning a desired feature detector, given the scarcity of quality thermal data. Code will be made available upon publication.

翻译：热成像为视觉退化环境（如低光照、烟雾或恶劣天气）下的视觉SLAM提供了一种实用的感知模态。然而，热成像通常具有纹理稀疏、对比度低和噪声高的特点，这使得基于特征的SLAM面临挑战。本研究提出一种面向热成像的稀疏单目图优化SLAM系统，该系统利用通用学习特征——在大规模可见光谱数据上训练的SuperPoint检测器与LightGlue匹配器，以提升跨域泛化能力。为使这些组件适应热成像数据，我们引入了预处理流程以增强输入适应性，并修改了核心SLAM模块以处理稀疏且易受异常值干扰的特征匹配。我们进一步将SuperPoint提取的关键点置信度分数整合至置信度加权因子图中，以提升估计鲁棒性。在公开热成像数据集上的评估表明，在高质量热数据稀缺的情况下，所提系统无需针对特定数据集进行训练或微调特征检测器即可实现可靠性能。代码将在论文发表时公开提供。