TRGS-SLAM: IMU-Aided Gaussian Splatting SLAM for Blurry, Rolling Shutter, and Noisy Thermal Images

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.

Figures (6)

• Figure 1: The main elements of TRGS-SLAM. Left: Microbolometer-aware rendering. Right: 3DGS map and B-spline trajectory.
• Figure 2: TRGS-SLAM system diagram
• Figure 3: Microbolometer-aware rendering process. A sum of multiple rasters, each multiplied with precomputed pixel-wise weights, is combined with a learned FPN estimate to obtain an approximation of the degraded input image.
• Figure 4: A depiction of the Gaussians targeted by our view-diversity-based opacity resetting. The color scale is mapped to the log of the condition number of $\mathbf{V}_g$ for each Gaussian.
• Figure 5: TRGS-SLAM result on the medium indoor sequence.