  Loop closing and relocalization are crucial techniques to establish reliable
and robust long-term SLAM by addressing pose estimation drift and degeneration.
This article begins by formulating loop closing and relocalization within a
unified framework. Then, we propose a novel multi-head network LCR-Net to
tackle both tasks effectively. It exploits novel feature extraction and
pose-aware attention mechanism to precisely estimate similarities and 6-DoF
poses between pairs of LiDAR scans. In the end, we integrate our LCR-Net into a
SLAM system and achieve robust and accurate online LiDAR SLAM in outdoor
driving environments. We thoroughly evaluate our LCR-Net through three setups
derived from loop closing and relocalization, including candidate retrieval,
closed-loop point cloud registration, and continuous relocalization using
multiple datasets. The results demonstrate that LCR-Net excels in all three
tasks, surpassing the state-of-the-art methods and exhibiting a remarkable
generalization ability. Notably, our LCR-Net outperforms baseline methods
without using a time-consuming robust pose estimator, rendering it suitable for
online SLAM applications. To our best knowledge, the integration of LCR-Net
yields the first LiDAR SLAM with the capability of deep loop closing and
relocalization. The implementation of our methods will be made open-source.
