Achieving real-time Simultaneous Localization and Mapping (SLAM) based on 3D Gaussian splatting (3DGS) in large-scale real-world environments remains challenging, as existing methods still struggle to jointly achieve low-latency pose estimation, continuous 3D Gaussian reconstruction, and long-term global consistency. In this paper, we present a tightly coupled LiDAR-Inertial-Visual 3DGS-based SLAM framework for real-time pose estimation and photorealistic mapping in large-scale real-world scenes. The system executes state estimation and 3D Gaussian primitive initialization in parallel with global Gaussian optimization, enabling continuous dense mapping. To improve Gaussian initialization quality and accelerate optimization convergence, we introduce a cascaded strategy that combines feed-forward predictions with geometric priors derived from voxel-based principal component analysis. To enhance global consistency, we perform loop closure directly on the optimized global Gaussian map by estimating loop constraints through Gaussian-based Generalized Iterative Closest Point registration, followed by pose-graph optimization. We also collect challenging large-scale looped outdoor sequences with hardware-synchronized LiDAR-camera-IMU and ground-truth trajectories for realistic evaluation. Extensive experiments on both public datasets and our dataset demonstrate that the proposed method achieves a state of the art among real-time efficiency, localization accuracy, and rendering quality across diverse real-world scenes.

翻译：基于三维高斯溅射（3DGS）的大规模真实环境实时同步定位与地图构建（SLAM）仍具挑战性，现有方法难以同时实现低延迟位姿估计、连续三维高斯重建与长期全局一致性。本文提出一种紧耦合的激光雷达-惯性-视觉3DGS SLAM框架，用于大规模真实场景中的实时位姿估计与逼真地图构建。该系统并行执行状态估计与三维高斯基元初始化及全局高斯优化，实现连续密集建图。为提升高斯初始化质量并加速优化收敛，我们引入级联策略，将前馈预测与基于体素主成分分析的几何先验相结合。为增强全局一致性，我们直接对优化后的全局高斯地图进行闭环检测——通过基于高斯的广义迭代最近点配准估计闭环约束，随后进行位姿图优化。我们还采集了具有硬件同步激光雷达-相机-IMU及真实轨迹的大规模闭环室外序列用于实际评估。在公开数据集与自建数据集上的大量实验表明，该方法在多种真实场景中均达到了实时性、定位精度与渲染质量的业界最优水平。