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.

翻译：全局地点识别与三维重定位是三维激光雷达同步定位与建图（SLAM）中闭环检测的最重要组成部分之一。为了通过特征匹配方法获取精确的全局六自由度变换，已有多种端到端架构被提出。然而，现有方法未考虑特征间的错误对应，导致不必要的特征也被纳入全局地点识别与重定位过程。本文提出一种鲁棒对应估计方法，通过同时剔除不必要的特征并突出必要的特征来实现目标。为聚焦必要特征并忽略非必要特征，我们利用三维激光雷达点云中两场景间的几何相关性。我们引入对应辅助损失函数，该函数基于点对齐算法寻找关键相关性，并支持所提网络在鲁棒对应估计下的端到端训练。由于包含大量平面块的地面在对应估计中常充当离群点，我们还提出一种预处理步骤，通过移除主导平面块来考虑负对应。在动态城市驾驶数据集上的评估结果表明，所提方法可同时提升全局地点识别与重定位任务的性能。实验证实，鲁棒特征对应估计是地点识别与重定位中的关键因素之一。