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.

翻译：回环闭合与重定位是解决位姿估计漂移与退化问题、构建可靠且稳健的长期SLAM的关键技术。本文首先将回环闭合与重定位统一于一个框架中，随后提出一种新型多头网络LCR-Net以高效处理这两项任务。该网络利用新颖的特征提取与位姿感知注意力机制，以精确估计LiDAR扫描对之间的相似度与6自由度位姿。最后，我们将LCR-Net集成至SLAM系统，实现了户外驾驶环境中稳健且准确的在线LiDAR SLAM。通过回环闭合与重定位衍生的三项设置（包括候选检索、闭环点云配准以及基于多数据集的连续重定位）对LCR-Net进行了全面评估。结果表明，LCR-Net在所有三项任务中均表现卓越，超越现有最先进方法，并展现出非凡的泛化能力。值得注意的是，LCR-Net无需使用耗时的稳健位姿估计器即可超越基线方法，因而适用于在线SLAM应用。据我们所知，LCR-Net的集成首次实现了具备深度回环闭合与重定位能力的LiDAR SLAM系统。相关方法将开源。