G3Reg: Pyramid Graph-based Global Registration using Gaussian Ellipsoid
  Model
Zhijian Qiao Zehuan Yu Binqian Jiang Huan Yin Shaojie Shen
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.