TRGS-SLAM：基于IMU的高斯点云SLAM用于模糊、滚动快门和噪声热图像
TRGS-SLAM: IMU-Aided Gaussian Splatting SLAM for Blurry, Rolling Shutter, and Noisy Thermal Images
AI总结：
本文提出TRGS-SLAM，一种基于3DGS的热惯性SLAM系统，能处理热图像中的模糊、滚动快门和噪声问题，通过改进的3DGS渲染方法和创新技术实现高精度跟踪。
摘要：
热相机在移动机器人同时定位与建图（SLAM）中具有优势：提供被动、低功耗的黑暗环境解决方案，对快速变化或高动态范围光照不变，并能穿透雾、尘和烟。然而，未冷却微bolometer热相机在大多数机器人应用中是唯一实用选项，但存在显著的运动模糊、滚动快门失真和固定模式噪声。本文提出TRGS-SLAM，一种基于3DGS的热惯性SLAM系统，能够处理这些退化。为克服热数据挑战，我们引入了模型感知的3DGS渲染方法，并对3DGS SLAM进行了多项创新，包括B样条轨迹优化（双阶段IMU损失）、基于视图多样性的不透明度重置和姿态漂移校正方案。我们的系统在现实世界快速运动和高噪声热数据上实现了准确跟踪，而其他测试SLAM方法均失败。此外，通过离线优化SLAM结果，我们展示了与先前需要地面真实姿态的图像修复相竞争的热图像修复效果。
英文摘要：
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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