融合热成像与激光雷达，提升光照变化下三维建图精度
LIT-GS: LiDAR-Inertial-Thermal Gaussian Splatting for Illumination-Robust Mapping
- 用激光雷达平面信息约束相机位姿和点云优化
- 热成像与激光雷达关联后，显著改善弱光场景建图
- 适合夜间、低纹理等极端光照条件下的导航系统
高斯点阵实现了实时神经渲染，但现有基于激光雷达-惯性-视觉（LIV）的高斯建图方法在光照变化和纹理缺失场景下仍表现脆弱，因其依赖于RGB光照一致性。本文提出LIT-GS框架，将激光雷达提取的平面几何作为显式约束，应用于位姿/结构优化及高斯点优化过程。具体地，利用LIV视觉地图点作为可信跨模态锚点，建立可靠的热成像-激光雷达对应关系，并在捆绑调整中引入加权激光点到平面残差，实现弱热监督下的相机位姿与3D点联合优化。在此优化结构基础上，进一步提出基于激光雷达平面正则化的可微点阵目标，约束渲染点与局部观测平面对齐，有效缓解低对比度热成像中的表面增厚与结构漂移问题。在自研序列与公开数据集上的实验表明，相较于最先进方法，LIT-GS在挑战性光照条件下持续提升几何精度与渲染质量。
原文摘要 · Abstract (English)
Gaussian Splatting has enabled real-time neural rendering, yet existing LiDAR-inertial-visual (LIV) Gaussian mapping pipelines remain fragile under illumination changes and texture-deficient scenes due to their reliance on RGB photometric cues. We present LIT-GS, a LiDAR-inertial-thermal Gaussian Splatting framework that injects LiDAR-derived plane geometry as an explicit constraint in both pose/structure refinement and Gaussian optimization. Specifically, we exploit LIV visual map points as confidence-aware cross-modal anchors to establish reliable thermal-LiDAR associations, and incorporate weighted LiDAR point-to-plane residuals into bundle adjustment to jointly refine camera poses and 3D points under weak thermal supervision. Building on the refined structure, we further introduce a LiDAR-plane-regularized differentiable splatting objective that constrains rendered 3D points to align with locally observed planes, mitigating surface thickening and structural drift in low-contrast thermal imagery. Experiments on proprietary sequences and public datasets demonstrate that LIT-GS consistently improves geometric accuracy and rendering quality over state-of-the-art LIV-based Gaussian Splatting baselines, particularly in challenging lighting conditions.
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