This study introduces a novel framework, G3Reg, for fast and robust global registration of LiDAR point clouds. In contrast to conventional complex keypoints and descriptors, we extract fundamental geometric primitives including planes, clusters, and lines (PCL) from the raw point cloud to obtain low-level semantic segments. Each segment is formulated as a unified Gaussian Ellipsoid Model (GEM) by employing a probability ellipsoid to ensure the ground truth centers are encompassed with a certain degree of probability. Utilizing these GEMs, we then present a distrust-and-verify scheme based on a Pyramid Compatibility Graph for Global Registration (PAGOR). Specifically, we establish an upper bound, which can be traversed based on the confidence level for compatibility testing to construct the pyramid graph. Gradually, we solve multiple maximum cliques (MAC) for each level of the graph, generating numerous transformation candidates. In the verification phase, we adopt a precise and efficient metric for point cloud alignment quality, founded on geometric primitives, to identify the optimal candidate. The performance of the algorithm is extensively validated on three publicly available datasets and a self-collected multi-session dataset, without changing any parameter settings in the experimental evaluation. The results exhibit superior robustness and real-time performance of the G3Reg framework compared to state-of-the-art methods. Furthermore, we demonstrate the potential for integrating individual GEM and PAGOR components into other algorithmic frameworks to enhance their efficacy. To advance further research and promote community understanding, we have publicly shared the source code.

翻译：本研究提出了一种新颖的框架G3Reg，用于快速鲁棒的激光雷达点云全局配准。与传统的复杂关键点与描述子不同，我们从原始点云中提取基本几何基元，包括平面、聚类和直线（PCL），以获得低层次语义片段。每个片段通过概率椭球建模为统一的高斯椭球模型（GEM），以确保以一定概率覆盖真实中心。基于这些GEM，我们提出了一种基于金字塔兼容性图的全局配准（PAGOR）的“怀疑-验证”方案。具体地，我们建立一个上界，可根据置信度水平在兼容性测试中遍历以构建金字塔图。逐步地，我们求解图中每一层的多个最大团（MAC），生成大量变换候选。在验证阶段，我们采用基于几何基元的精确高效点云对齐质量度量来识别最优候选。该算法性能在三个公开数据集和一个自采集的多会话数据集上进行了广泛验证，且在实验评估中未更改任何参数设置。结果表明，相比最先进方法，G3Reg框架展现出卓越的鲁棒性和实时性能。此外，我们展示了将单个GEM和PAGOR组件集成到其他算法框架中以增强其效能的潜力。为促进进一步研究和社区理解，我们已公开共享源代码。