Abstract
Thermal cameras offer several advantages for simultaneous localization and mapping (SLAM) with mobile robots: they provide a passive, low-power solution to operating in darkness, are invariant to rapidly changing or high dynamic range illumination, and can see through fog, dust, and smoke. However, uncooled microbolometer thermal cameras, the only practical option in most robotics applications, suffer from significant motion blur, rolling shutter distortions, and fixed pattern noise. In this paper, we present TRGS-SLAM, a 3D Gaussian Splatting (3DGS) based thermal inertial SLAM system uniquely capable of handling these degradations. To overcome the challenges of thermal data, we introduce a model-aware 3DGS rendering method and several general innovations to 3DGS SLAM, including B-spline trajectory optimization with a two-stage IMU loss, view-diversity-based opacity resetting, and pose drift correction schemes. Our system demonstrates accurate tracking on real-world, fast motion, and high-noise thermal data that causes all other tested SLAM methods to fail. Moreover, through offline refinement of our SLAM results, we demonstrate thermal image restoration competitive with prior work that required ground truth poses.
Abstract (translated)
热成像相机为移动机器人同时定位与地图构建（SLAM）提供了多项优势：它们提供了一种在黑暗中运行的被动式低功耗解决方案，不受快速变化或高动态范围光照的影响，且能够穿透雾、灰尘和烟雾。然而，在大多数机器人应用中唯一实用的非制冷微测辐射热计热成像相机，却存在严重的运动模糊、卷帘快门畸变和固定模式噪声问题。本文提出TRGS-SLAM，一种基于3D高斯泼溅（3DGS）的热惯性SLAM系统，独特地能够处理这些退化问题。为克服热成像数据的挑战，我们引入了模型感知的3DGS渲染方法，并对3DGS SLAM进行了多项通用创新，包括采用两阶段IMU损失的B样条轨迹优化、基于视图多样性的透明度重置机制，以及位姿漂移校正方案。我们的系统在真实世界的高速运动和高噪声热成像数据上展现出精确的跟踪性能，这些数据导致所有其他测试的SLAM方法失效。此外，通过对SLAM结果进行离线优化，我们展示了与需要真实位姿的先前工作具有竞争力的热成像图像恢复效果。
URL
https://arxiv.org/abs/2603.20443
