  Global place recognition and 3D relocalization are one of the most important
components in the loop closing detection for 3D LiDAR Simultaneous Localization
and Mapping (SLAM). In order to find the accurate global 6-DoF transform by
feature matching approach, various end-to-end architectures have been proposed.
However, existing methods do not consider the false correspondence of the
features, thereby unnecessary features are also involved in global place
recognition and relocalization. In this paper, we introduce a robust
correspondence estimation method by removing unnecessary features and
highlighting necessary features simultaneously. To focus on the necessary
features and ignore the unnecessary ones, we use the geometric correlation
between two scenes represented in the 3D LiDAR point clouds. We introduce the
correspondence auxiliary loss that finds key correlations based on the point
align algorithm and enables end-to-end training of the proposed networks with
robust correspondence estimation. Since the ground with many plane patches acts
as an outlier during correspondence estimation, we also propose a preprocessing
step to consider negative correspondence by removing dominant plane patches.
The evaluation results on the dynamic urban driving dataset, show that our
proposed method can improve the performances of both global place recognition
and relocalization tasks. We show that estimating the robust feature
correspondence is one of the important factors in place recognition and
relocalization.
