3D Gaussian Splatting (3DGS) has shown its ability in rapid rendering and high-fidelity mapping. In this paper, we introduce LVI-GS, a tightly-coupled LiDAR-Visual-Inertial mapping framework with 3DGS, which leverages the complementary characteristics of LiDAR and image sensors to capture both geometric structures and visual details of 3D scenes. To this end, the 3D Gaussians are initialized from colourized LiDAR points and optimized using differentiable rendering. In order to achieve high-fidelity mapping, we introduce a pyramid-based training approach to effectively learn multi-level features and incorporate depth loss derived from LiDAR measurements to improve geometric feature perception. Through well-designed strategies for Gaussian-Map expansion, keyframe selection, thread management, and custom CUDA acceleration, our framework achieves real-time photo-realistic mapping. Numerical experiments are performed to evaluate the superior performance of our method compared to state-of-the-art 3D reconstruction systems.
3D Gaussian Splatting（3DGS）在快速渲染和高保真映射方面展现了其能力。本文提出了LVI-GS，一种紧耦合的LiDAR-视觉-惯性（LiDAR-Visual-Inertial）映射框架，结合3DGS的优势，利用LiDAR和图像传感器的互补特性来捕捉3D场景的几何结构和视觉细节。为此，我们从彩色化的LiDAR点初始化3D高斯，并通过可微分渲染进行优化。为实现高保真映射，我们引入了一种基于金字塔的训练方法，有效学习多层次特征，并结合从LiDAR测量获得的深度损失，以提升几何特征感知能力。通过精心设计的高斯图扩展、关键帧选择、线程管理和自定义CUDA加速策略，我们的框架实现了实时逼真的映射效果。数值实验验证了我们的方法相较于最先进的3D重建系统的卓越性能。