Fast and Accurate Deep Loop Closing and Relocalization for Reliable
  LiDAR SLAM
Chenghao Shi Xieyuanli Chen Junhao Xiao Bin Dai Huimin Lu
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.