LIT-GS: 面向光照鲁棒建图的激光雷达-惯性-热高斯泼溅
LIT-GS: LiDAR-Inertial-Thermal Gaussian Splatting for Illumination-Robust Mapping
AI总结：
提出LIT-GS框架，利用激光雷达平面几何约束联合优化位姿与高斯，解决光照变化和纹理缺失场景下RGB依赖的脆弱性问题，提升几何精度与渲染质量。
摘要：
高斯泼溅实现了实时神经渲染，但现有的激光雷达-惯性-视觉（LIV）高斯建图流程由于依赖RGB光度线索，在光照变化和纹理缺失场景下仍然脆弱。我们提出了LIT-GS，一个激光雷达-惯性-热高斯泼溅框架，将激光雷达导出的平面几何作为显式约束注入到位姿/结构优化和高斯优化中。具体来说，我们利用LIV视觉地图点作为置信度感知的跨模态锚点，建立可靠的热-激光雷达关联，并在弱热监督下将加权的激光雷达点到平面残差引入光束法平差，以联合优化相机位姿和3D点。基于优化后的结构，我们进一步引入一个激光雷达平面正则化的可微泼溅目标，约束渲染的3D点与局部观测平面对齐，从而减轻低对比度热图像中的表面增厚和结构漂移。在专有序列和公开数据集上的实验表明，LIT-GS在几何精度和渲染质量上持续优于最先进的基于LIV的高斯泼溅基线，尤其是在具有挑战性的光照条件下。
英文摘要：
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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