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 represented as a unified Gaussian Ellipsoid Model (GEM), using a probability ellipsoid to ensure the ground truth centers are encompassed with a certain degree of probability. Utilizing these GEMs, we 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. Then, we solve multiple maximum cliques (MAC) for each level of the pyramid graph, thus generating the corresponding 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 algorithm's performance is validated on three publicly available datasets and a self-collected multi-session dataset. Parameter settings remained unchanged during the experiment evaluations. 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 registration frameworks to enhance their efficacy. Code: https://github.com/HKUST-Aerial-Robotics/G3Reg

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。与传统复杂的特征点和描述子不同，我们从原始点云中提取包括平面、簇和直线在内的基础几何基元，以获得低层次语义片段。每个片段用统一的高斯椭球模型表示，通过概率椭球确保真实中心以一定概率被包含在内。基于这些高斯椭球模型，我们提出了一种基于金字塔兼容性图的全局配准不信任与验证方案。具体而言，我们建立了一个上界，该上界可基于置信度遍历以构建金字塔图，进而求解金字塔图每一层的多个最大团以生成相应的变换候选。在验证阶段，我们采用一种基于几何基元的精确高效点云对齐质量度量，以识别最优候选。算法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验证，实验评估中参数设置保持不变。结果表明，与现有最先进方法相比，G3Reg框架具有更强的鲁棒性和实时性。此外，我们展示了将独立的高斯椭球模型和PAGOR组件集成到其他配准框架中以提升其效能的潜力。代码：https://github.com/HKUST-Aerial-Robotics/G3Reg