arXiv 2307.082212024-03-20cs.RO

NDT-Map-Code：一种用于激光SLAM实时闭环检测的3D全局描述子

NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM

The College of Mechanical and Vehicle Engineering, Chongqing University（重庆大学机械与车辆工程学院）；NIO（蔚来）；The State Key Lab of Mechanical Transmissions, Chongqing University（重庆大学机械传动国家重点实验室）；Department of Computer Science, The University of Sheffield（谢菲尔德大学计算机科学系）

Lizhou Liao, Wenlei Yan, Li Sun, Xinhui Bai, Zhenxing You, Hongyuan Yuan, Chunyun Fu

备注：: 8 pages, 6 figures, 4 tables

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AI总结：

本文提出一种基于NDT的3D全局描述子NDT-Map-Code，无需密集点云即可直接从NDT地图提取特征，用于道路与地下泊车场景的激光SLAM实时闭环检测，性能显著优于现有最先进方法。

摘要：

闭环检测，也称位置识别，旨在识别曾访问过的位置，是SLAM系统的关键组件。现有基于激光雷达的闭环检测研究严重依赖密集点云和360度FOV激光雷达。本文提出一种基于NDT（正态分布变换）的开箱即用全局描述子NDT-Map-Code，专为道路驾驶和地下代客泊车场景设计。它可直接从NDT地图中提取，无需密集点云，具备出色的可扩展性和低维护成本。该方法利用NDT表示来识别代表性模式，并根据其空间位置（方位、距离和高度）进行编码。在NIO地下停车场数据集和KITTI数据集上的实验结果表明，与最先进方法相比，该方法取得了显著更优的性能。

英文摘要：

Loop-closure detection, also known as place recognition, aiming to identify previously visited locations, is an essential component of a SLAM system. Existing research on lidar-based loop closure heavily relies on dense point cloud and 360 FOV lidars. This paper proposes an out-of-the-box NDT (Normal Distribution Transform) based global descriptor, NDT-Map-Code, designed for both on-road driving and underground valet parking scenarios. NDT-Map-Code can be directly extracted from the NDT map without the need for a dense point cloud, resulting in excellent scalability and low maintenance cost. The NDT representation is leveraged to identify representative patterns, which are further encoded according to their spatial location (bearing, range, and height). Experimental results on the NIO underground parking lot dataset and the KITTI dataset demonstrate that our method achieves significantly better performance compared to the state-of-the-art.

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