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：回环检测（亦称位置识别）旨在识别先前访问过的位置，是SLAM系统中的关键组成部分。现有基于激光雷达的回环检测研究严重依赖密集点云技术和360°视场角激光雷达。本文提出一种即插即用的基于NDT（正态分布变换）的全局描述子NDT-Map-Code，该描述子同时适用于道路行驶和地下代客泊车场景。NDT-Map-Code可直接从NDT地图中提取而无需密集点云，具有卓越的扩展性和低维护成本。该方法利用NDT表征识别代表性模式，并根据其空间位置（方位角、距离和高度）对这些模式进行进一步编码。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，我们的方法实现了显著更优的性能。