论文标题
基因座：基于激光雷达的位置识别使用时空高阶合并
Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling
论文作者
论文摘要
位置识别能够通过在同时定位和映射（SLAM）中提供非本地约束来估计全球一致的图和轨迹。本文介绍了使用3D激光点云在大规模环境中使用3D激光点云的新颖地点识别方法。我们提出了一种方法，用于提取和编码与场景中与组件相关的拓扑和时间信息，并演示将此辅助信息包含在适当的情况下，描述说明会导致更强大和更具歧视性的场景表示形式。二阶合并以及非线性变换用于汇总这些多级特征，以生成固定长度的全局描述符，这对于输入特征的排列不变。所提出的方法优于Kitti数据集上的最先进方法。此外，在几种具有挑战性的情况下，诸如3D激光点云中的遮挡和观点变化等几种具有挑战性的情况下，基因座被证明是可靠的。开源实现可在以下网址获得：https：//github.com/csiro-robotics/locus。
Place Recognition enables the estimation of a globally consistent map and trajectory by providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This paper presents Locus, a novel place recognition method using 3D LiDAR point clouds in large-scale environments. We propose a method for extracting and encoding topological and temporal information related to components in a scene and demonstrate how the inclusion of this auxiliary information in place description leads to more robust and discriminative scene representations. Second-order pooling along with a non-linear transform is used to aggregate these multi-level features to generate a fixed-length global descriptor, which is invariant to the permutation of input features. The proposed method outperforms state-of-the-art methods on the KITTI dataset. Furthermore, Locus is demonstrated to be robust across several challenging situations such as occlusions and viewpoint changes in 3D LiDAR point clouds. The open-source implementation is available at: https://github.com/csiro-robotics/locus .
