Abstract
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.
Abstract (translated)
热红外传感器，其波长比烟雾颗粒长，能够捕捉不受黑暗、灰尘和烟雾影响的图像。这种鲁棒性使它们在机器人运动估计和环境感知中变得越来越有价值，尤其是在恶劣条件下。然而，现有的热里程计和映射方法主要是几何学上的，并且经常在不同的数据集中失效，同时缺乏生成密集地图的能力。受最近高斯点阵（GS）技术的效率和高质量重建能力启发，我们提出了TOM-GS，这是一种将基于学习的里程计与基于GS的密集映射集成在一起的热里程计和映射方法。TOM-GS是首个专为热相机设计并采用GS为基础的SLAM系统的之一，它具有专门针对热图像增强及单目深度整合的功能。在运动估计和新视角渲染方面的广泛实验表明，TOM-GS优于现有的基于学习的方法，确认了基于学习的工作流程对于稳健的热里程计和密集重建的好处。
URL
https://arxiv.org/abs/2602.07493
