LIT-GS: 照明変動に頑健なマッピングのためのLiDAR-慣性-熱ガウシアンスプラッティング
LIT-GS: LiDAR-Inertial-Thermal Gaussian Splatting for Illumination-Robust Mapping
RGBの光度情報に依存せず、LiDAR由来の平面幾何を明示的な制約として組み込んだ、照明変化やテクスチャ欠損に頑健なLiDAR-慣性-熱ガウシアンスプラッティングマッピングフレームワークを提案した。
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著者: Shikuan Shi, Chunran Zheng, Jiaming Xu, Tianyong Ye, Tao Yu, Yukang Cui
分類: cs.RO
原文アブストラクト
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.
