While 3D Gaussian Splatting (3DGS) enabled photorealistic mapping, its integration into SLAM has largely followed traditional camera-centric pipelines. As a result, they inherit well-known weaknesses such as high computational load, failure in texture-poor or illumination-varying environments, and limited operational range, particularly for RGB-D setups. On the other hand, LiDAR emerges as a robust alternative, but its integration with 3DGS introduces new challenges, such as the need for tighter global alignment for photorealistic quality and prolonged optimization times caused by sparse data. To address these challenges, we propose LIVE-GS, an online LiDAR-Inertial Visual SLAM framework that tightly couples 3D Gaussian Splatting with LiDAR-based surfels to ensure high-precision map consistency through global geometric optimization. Particularly, to handle sparse data, our system employs a depth-invariant Gaussian initialization strategy for efficient representation and a bounded sigmoid constraint to prevent uncontrolled Gaussian growth. Experiments on public and our datasets demonstrate competitive performance in rendering quality and map-building efficiency compared with representative 3DGS SLAM baselines.

翻译：尽管3D高斯泼溅（3DGS）技术实现了逼真的建图效果，但其在SLAM系统中的集成大多沿用了传统的以相机为中心的流程。因此，这些系统继承了众所周知的缺陷，例如高计算负载、在纹理贫乏或光照变化环境中的失效，以及有限的工作范围（尤其对于RGB-D配置）。另一方面，激光雷达作为一种鲁棒的替代方案出现，但其与3DGS的集成带来了新的挑战，例如为实现逼真质量所需的更严格全局配准，以及稀疏数据导致的优化时间延长。为应对这些挑战，我们提出了LIVE-GS——一个在线激光雷达-惯性-视觉SLAM框架，通过将3D高斯泼溅与基于激光雷达的面元紧密耦合，并借助全局几何优化来确保高精度地图一致性。特别地，为处理稀疏数据，我们的系统采用深度不变的高斯初始化策略以实现高效表征，并引入有界Sigmoid约束以防止高斯分布的无控制增长。在公开数据集及我们自有数据集上的实验表明，相较于代表性的3DGS SLAM基线方法，本系统在渲染质量与建图效率方面均展现出竞争优势。