Abstract
While 3D Gaussian Splatting (3DGS) has revolutionized photorealistic mapping, conventional approaches based on camera sensor, even RGB-D, suffer from fundamental limitations such as high computational load, failure in environments with poor texture or illumination, and short operational ranges. LiDAR emerges as a robust alternative, but its integration with 3DGS introduces new challenges, such as the need for exceptional global alignment for photorealistic quality and prolonged optimization times caused by sparse data. To address these challenges, we propose GSFusion, an online LiDAR-Inertial-Visual mapping system that ensures high-precision map consistency through a surfel-to-surfel constraint in the global pose-graph optimization. To handle sparse data, our system employs a pixel-aware Gaussian initialization strategy for efficient representation and a bounded sigmoid constraint to prevent uncontrolled Gaussian growth. Experiments on public and our datasets demonstrate our system outperforms existing 3DGS SLAM systems in terms of rendering quality and map-building efficiency.
Abstract (translated)
尽管三维高斯点阵法（3DGS）已经革新了逼真的地图绘制技术，但基于相机传感器的传统方法，甚至是RGB-D方法，仍然存在诸如计算负荷重、在纹理或光照条件差的环境中表现不佳以及工作范围有限等根本性限制。激光雷达（LiDAR）作为稳健的选择出现了，然而将其与3DGS结合时会带来新的挑战，例如为了实现逼真的效果需要进行卓越的整体对齐，并且由于数据稀疏导致优化时间延长。 为了解决这些问题，我们提出了一种在线的LiDAR-惯性-视觉地图系统——GSFusion。该系统通过在全局姿态图优化中采用点阵到点阵约束来确保高精度的地图一致性。为了处理稀疏的数据，我们的系统使用像素感知的高斯初始化策略来进行高效的表示，并采用了有界Sigmoid约束以防止高斯增长失控。 实验结果表明，在公共数据集和我们自己的数据集中，我们的系统在渲染质量和地图构建效率方面都优于现有的3DGS SLAM（即时定位与地图构建）系统。
URL
https://arxiv.org/abs/2507.23273
