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.

翻译：闭环检测又称位置识别，旨在识别先前访问过的位置，是SLAM系统的重要组成部分。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度视场角激光雷达。本文提出一种即插即用的基于正态分布变换的全局描述子NDT-Map-Code，适用于道路驾驶与地下代客泊车两种场景。该描述子可直接从NDT地图中提取，无需依赖密集点云，因而具备卓越的可扩展性和低维护成本。通过利用NDT表示识别典型模式，并根据空间位置（方位角、距离和高度）对模式进行编码。在蔚来地下停车场数据集和KITTI数据集上的实验结果表明，与现有最先进方法相比，本方法取得了显著更优的性能。