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.

该研究介绍了一个新的框架 G3Reg，用于快速而稳健地全局注册激光雷达点云。与传统的复杂关键点和描述符不同，我们从原始点云中提取基本的几何原语，包括平面、聚类和线段，以获取低级语义分段。每个分段都被形式化为一个统一的高斯椭球模型 (GEM)，通过采用概率椭球来确保地面事实上的中心被一定概率范围内涵盖。利用这些 GEMs，我们提出了一种基于金字塔兼容性图的不信任和验证的方案 (PAGOR)。具体而言，我们建立了一个上界，可以基于置信水平进行遍历，以用于构建金字塔图的兼容性测试。逐渐地，我们针对图的每个层解决多个最大团 (MAC)，生成许多转换候选。在验证阶段，我们采用了一种基于几何原语的精确而高效的评估指标，用于识别最佳候选。该算法的性能在三个公开数据集和一个经过自行收集的多会话数据集上进行了广泛验证，在实验评估中未改变任何参数设置。结果表明，与最先进的方法相比，G3Reg 框架具有卓越的稳健性和实时性能。此外，我们展示了将个别的 GEM 和 PAGOR 组件集成到其他算法框架中以增强其效力的潜力。为了推动进一步的研究并促进社区理解，我们已公开共享源代码。