Mapping and localization are crucial problems in robotics and autonomous driving. Recent advances in 3D Gaussian Splatting (3DGS) have enabled precise 3D mapping and scene understanding by rendering photo-realistic images. However, existing 3DGS methods often struggle to accurately reconstruct a 3D map that reflects the actual scale and geometry of the real world, which degrades localization performance. To address these limitations, we propose a novel 3DGS method called Geometry-Aware Gaussian Splatting (GeomGS). This method fully integrates LiDAR data into 3D Gaussian primitives via a probabilistic approach, as opposed to approaches that only use LiDAR as initial points or introduce simple constraints for Gaussian points. To this end, we introduce a Geometric Confidence Score (GCS), which identifies the structural reliability of each Gaussian point. The GCS is optimized simultaneously with Gaussians under probabilistic distance constraints to construct a precise structure. Furthermore, we propose a novel localization method that fully utilizes both the geometric and photometric properties of GeomGS. Our GeomGS demonstrates state-of-the-art geometric and localization performance across several benchmarks, while also improving photometric performance.
地图构建和定位是机器人技术和自动驾驶中的关键问题。最近，3D高斯点云（3DGS）的进展使得通过渲染照片级真实感图像，能够实现精确的3D地图构建和场景理解。然而，现有的3DGS方法通常难以准确重建反映真实世界实际尺度和几何的3D地图，从而影响定位性能。为了解决这些问题，我们提出了一种新颖的3DGS方法——几何感知高斯点云（Geometry-Aware Gaussian Splatting，GeomGS）。该方法通过概率方法将LiDAR数据完全整合到3D高斯原语中，而不是仅将LiDAR作为初始点或为高斯点引入简单约束。为此，我们引入了几何置信度评分（Geometric Confidence Score，GCS），用于识别每个高斯点的结构可靠性。GCS与高斯点一起，在概率距离约束下进行优化，从而构建出精确的结构。此外，我们还提出了一种新型的定位方法，充分利用GeomGS的几何和光度特性。我们的GeomGS方法在多个基准测试中展示了最先进的几何和定位性能，同时也提高了光度性能。