In this paper, we introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), our approach enables real-time photo-realistic mapping while ensuring high-quality image rendering in large-scale unbounded outdoor environments. In this work, Gaussian Process Regression (GPR) is employed to mitigate the issues resulting from sparse and unevenly distributed LiDAR observations. The voxel-based 3D Gaussians map representation facilitates real-time dense mapping in large outdoor environments with acceleration governed by custom CUDA kernels. Moreover, the overall framework is designed in a covariance-centered manner, where the estimated covariance is used to initialize the scale and rotation of 3D Gaussians, as well as update the parameters of the GPR. We evaluate our algorithm on several outdoor datasets, and the results demonstrate that our method achieves state-of-the-art performance in terms of mapping efficiency and rendering quality. The source code is available on GitHub.
本文提出了GS-LIVM，这是一种基于高斯点云的实时写实LiDAR-惯性-视觉（LIV）映射框架，专为户外场景设计。与基于神经辐射场（NeRF）和3D高斯点云（3DGS）的现有方法相比，我们的方法能够在大规模无限制的户外环境中实现实时的写实映射，并确保高质量的图像渲染。为解决稀疏且不均匀分布的LiDAR观测带来的问题，我们采用高斯过程回归（GPR）。基于体素的3D高斯表示支持在大型户外环境中进行实时密集映射，并通过自定义CUDA内核进行加速。此外，整个框架以协方差为核心，估计的协方差用于初始化3D高斯的尺度和旋转，并更新GPR的参数。我们在多个户外数据集上对该算法进行了评估，结果显示该方法在映射效率和渲染质量方面达到了最先进的性能。