LIT-GS: LiDAR-Inertial-Thermal Gaussian Splatting for Illumination-Robust Mapping
For autonomous systems operating in challenging lighting conditions, this work addresses the fragility of existing Gaussian mapping pipelines by incorporating thermal sensing and LiDAR plane constraints.
LIT-GS introduces a LiDAR-inertial-thermal Gaussian Splatting framework that uses LiDAR-derived plane geometry to improve mapping robustness under illumination changes and texture-deficient scenes, achieving consistent improvements in geometric accuracy and rendering quality over state-of-the-art LIV-based baselines.
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.