用通用特征提取器实现热成像下的稳定定位与建图
Thegra: Graph-based SLAM for Thermal Imagery
- 基于SuperPoint和LightGlue的通用特征匹配，跨域适应热成像
- 预处理+置信度加权因子图，提升稀疏噪声数据下的定位鲁棒性
- 无需热成像特定训练，适合低纹理、高噪声场景应用
热成像在低光照、烟雾或恶劣天气等视觉退化环境中具有实用价值，但其通常纹理稀疏、对比度低且噪声高，使基于特征的视觉SLAM难以实现。本文提出一种针对热成像的稀疏单目图优化SLAM系统，采用在大规模可见光数据上训练的通用特征提取器（SuperPoint检测器与LightGlue匹配器），以增强跨域泛化能力。为适配热成像特性，设计了输入预处理流程，并改进核心SLAM模块以应对稀疏且含异常值的特征匹配。进一步将SuperPoint的关键点置信度引入置信度加权因子图，提升状态估计鲁棒性。在公开热成像数据集上的评估表明，该系统无需特定数据集训练或微调特征检测器，即可实现可靠性能，适用于高质量热成像数据稀缺的场景。代码将在发表后公开。
原文摘要 · Abstract (English)
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.
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