解决热成像模糊、滚动快门畸变和噪声问题，实现高精度定位与建图。
TRGS-SLAM: IMU-Aided Gaussian Splatting SLAM for Blurry, Rolling Shutter, and Noisy Thermal Images
- 基于3D高斯点云渲染，融合惯性数据优化轨迹
- 实测在高速运动与强噪声下仍能稳定跟踪
- 适合机器人在黑暗、烟雾等复杂环境使用
热成像相机为移动机器人同时定位与地图构建（SLAM）提供被动、低功耗的夜间工作能力，对光照剧烈变化或高动态范围环境具有不变性，并可穿透雾、尘、烟。然而，大多数机器人应用中唯一可行的非制冷微测辐射热计热成像仪存在显著运动模糊、滚动快门畸变和固定模式噪声。本文提出TRGS-SLAM，一种基于3D高斯点云渲染（3DGS）的热惯性SLAM系统，可有效应对这些退化问题。通过引入模型感知的3DGS渲染方法，以及多项通用创新：基于B样条的轨迹优化、两阶段惯性损失设计、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，本系统在真实世界高速运动与高噪声热成像数据上实现了精准跟踪，而其他测试的SLAM方法均失效。此外，通过离线优化结果，其热图像恢复效果达到与依赖真值位姿的先前工作相当水平。
原文摘要 · Abstract (English)
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.
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