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:全球地点识别和3D 重新定位是3D LiDAR 同步本地化和绘图(SLAM) 环形闭合探测中最重要的组成部分之一。为了找到精确的全球 6-DoF 转换,通过特征匹配方法找到准确的全球 6-DoF 功能转换,提出了各种端对端结构。但是,现有方法并不考虑特征的虚假对应,因此,全球地点识别和重新定位也涉及不必要的特征。在本文件中,我们引入了一种强有力的通信估计方法,方法是同时消除不必要的特征并突出必要的特征。为了侧重于必要的特征并忽略不必要的特征,我们使用了3D LIDAR 点云中两个场景之间的几何对应关系。我们引入了基于点对齐的算法找到关键关联的通信辅助损失,从而能够对拟议网络进行端对端培训,同时进行可靠的通信估计。由于许多平面补丁的地面在通信估算中起到外围作用,我们还提议采取一个预处理步骤,通过消除占支配地位的平面补接而考虑负面对应之处。关于动态城市驱动数据集的评价结果,表明我们提出的方法可以改进全球特征识别和重新定位因素。</s>