Thermal infrared sensors, with wavelengths longer than smoke particles, can capture imagery independent of darkness, dust, and smoke. This robustness has made them increasingly valuable for motion estimation and environmental perception in robotics, particularly in adverse conditions. Existing thermal odometry and mapping approaches, however, are predominantly geometric and often fail across diverse datasets while lacking the ability to produce dense maps. Motivated by the efficiency and high-quality reconstruction ability of recent Gaussian Splatting (GS) techniques, we propose TOM-GS, a thermal odometry and mapping method that integrates learning-based odometry with GS-based dense mapping. TOM-GS is among the first GS-based SLAM systems tailored for thermal cameras, featuring dedicated thermal image enhancement and monocular depth integration. Extensive experiments on motion estimation and novel-view rendering demonstrate that TOM-GS outperforms existing learning-based methods, confirming the benefits of learning-based pipelines for robust thermal odometry and dense reconstruction.

翻译：热红外传感器因其波长大于烟雾颗粒，能够在黑暗、灰尘和烟雾环境下独立捕获图像。这种鲁棒性使其在机器人运动估计与环境感知中日益重要，尤其在恶劣条件下。然而，现有的热学里程计与建图方法主要基于几何原理，常在不同数据集上失效，且无法生成稠密地图。受近期高斯溅射技术高效性与高质量重建能力的启发，我们提出了TOM-GS——一种将基于学习的里程计与基于高斯溅射的稠密建图相结合的热学里程计与建图方法。TOM-GS是首批专为热成像相机设计的高斯溅射SLAM系统之一，具备专用的热图像增强与单目深度集成功能。在运动估计与新视角渲染方面的大量实验表明，TOM-GS优于现有基于学习的方法，证实了基于学习流程在鲁棒热学里程计与稠密重建中的优势。