  Global registration using 3D point clouds is a crucial technology for mobile
platforms to achieve localization or manage loop-closing situations. In recent
years, numerous researchers have proposed global registration methods to
address a large number of outlier correspondences. Unfortunately, the
degeneracy problem, which represents the phenomenon in which the number of
estimated inliers becomes lower than three, is still potentially inevitable. To
tackle the problem, a degeneracy-robust decoupling-based global registration
method is proposed, called Quatro. In particular, our method employs
quasi-SO(3) estimation by leveraging the Atlanta world assumption in urban
environments to avoid degeneracy in rotation estimation. Thus, the minimum
degree of freedom (DoF) of our method is reduced from three to one. As verified
in indoor and outdoor 3D LiDAR datasets, our proposed method yields robust
global registration performance compared with other global registration
methods, even for distant point cloud pairs. Furthermore, the experimental
results confirm the applicability of our method as a coarse alignment. Our code
is available: https://github.com/url-kaist/quatro.
