LIT-GS: LiDAR-Inertial-Thermal Gaussian Splatting for Illumination-Robust Mapping

2026-06-18

AI Summary

This work addresses the performance degradation of existing LiDAR-inertial-visual Gaussian splatting methods in weakly textured or drastically varying illumination scenarios, where reliance on RGB photometric information proves insufficient. To overcome this limitation, we propose a novel Gaussian splatting framework that fuses LiDAR, inertial, and thermal imaging modalities. Our approach explicitly embeds planar geometry extracted from LiDAR into the optimization pipeline by enforcing point-to-plane residual constraints on pose and structure estimation. We further introduce cross-modal anchor points to establish robust correspondences between thermal images and LiDAR data. A joint optimization strategy supervised by thermal cues, combined with a plane-regularized differentiable rendering objective, effectively mitigates surface bloating and structural drift caused by low-contrast thermal imagery. Experiments demonstrate that our method significantly outperforms state-of-the-art approaches on both self-collected and public datasets, achieving superior geometric accuracy and rendering fidelity under challenging lighting conditions.

Abstract

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.

Problem

• Gaussian Splatting
• illumination robustness
• LiDAR-inertial-visual mapping
• texture-deficient scenes
• thermal imagery

Innovation

• Gaussian Splatting
• LiDAR-inertial-thermal fusion
• plane regularization
• illumination-robust mapping
• cross-modal association