  Global registration is a fundamental task that estimates the relative pose
between two viewpoints of 3D point clouds. However, there are two issues that
degrade the performance of global registration in LiDAR SLAM: one is the
sparsity issue and the other is degeneracy. The sparsity issue is caused by the
sparse characteristics of the 3D point cloud measurements in a mechanically
spinning LiDAR sensor. The degeneracy issue sometimes occurs because the
outlier-rejection methods reject too many correspondences, leaving less than
three inliers. These two issues have become more severe as the pose discrepancy
between the two viewpoints of 3D point clouds becomes greater. To tackle these
problems, we propose a robust global registration framework, called
\textit{Quatro++}. Extending our previous work that solely focused on the
global registration itself, we address the robust global registration in terms
of the loop closing in LiDAR SLAM. To this end, ground segmentation is
exploited to achieve robust global registration. Through the experiments, we
demonstrate that our proposed method shows a higher success rate than the
state-of-the-art global registration methods, overcoming the sparsity and
degeneracy issues. In addition, we show that ground segmentation significantly
helps to increase the success rate for the ground vehicles. Finally, we apply
our proposed method to the loop closing module in LiDAR SLAM and confirm that
the quality of the loop constraints is improved, showing more precise mapping
results. Therefore, the experimental evidence corroborated the suitability of
our method as an initial alignment in the loop closing. Our code is available
at https://quatro-plusplus.github.io.
