用激光雷达提升3D高斯点云的几何精度，增强机器人定位能力
GeomGS: LiDAR-Guided Geometry-Aware Gaussian Splatting for Robot Localization
- 将激光雷达数据以概率方式融合到高斯点中，优化结构可靠性
- 提出几何置信度评分，同步优化点云结构与距离约束
- 兼顾几何与视觉信息，显著提升定位与建图性能
地图构建与定位是机器人和自动驾驶中的关键问题。近年来，3D高斯点云渲染（3DGS）技术通过生成逼真图像实现了高精度3D建图与场景理解。然而，现有方法在还原真实世界尺度与几何结构方面存在不足，影响了定位性能。为此，我们提出一种新型3DGS方法——几何感知高斯点云（GeomGS），通过概率方法将激光雷达（LiDAR）数据深度融合至3D高斯原始体中，而非仅作为初始点或施加简单约束。为此，我们引入几何置信度评分（GCS），用于评估每个高斯点的结构可靠性，并在概率距离约束下与高斯点同步优化，构建精确结构。此外，我们提出一种新定位方法，充分结合GeomGS的几何与光度特性。实验表明，GeomGS在多个基准测试中均达到最先进的几何重建与定位性能，同时提升了光度表现。
原文摘要 · Abstract (English)
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.
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