Consistent maps are key for most autonomous mobile robots, and they often use SLAM approaches to build such maps. Loop closures via place recognition help to maintain accurate pose estimates by mitigating global drift, and are thus key for realizing an effective SLAM system. This paper presents a robust loop closure detection pipeline for outdoor SLAM with LiDAR-equipped robots. Our method handles various LiDAR sensors with different scanning patterns, fields of view, and resolutions. It generates local maps from LiDAR scans and aligns them using a ground alignment module to handle both planar and non-planar motion of the LiDAR, ensuring applicability across platforms. The method uses density-preserving bird's-eye-view projections of these local maps and extracts ORB feature descriptors for place recognition. It stores the feature descriptors in a binary search tree for efficient retrieval, and self-similarity pruning addresses perceptual aliasing in repetitive environments. Extensive experiments on public and self-recorded datasets demonstrate accurate loop closure detection, long-term localization, and cross-platform multi-map alignment, agnostic to the LiDAR scanning patterns, fields of view, and motion profiles. We provide the code for our pipeline as open-source software at https://github.com/PRBonn/MapClosures.

翻译：一致的地图对于大多数自主移动机器人至关重要，而这类机器人通常采用SLAM方法来构建此类地图。通过位置识别实现的闭环检测有助于抑制全局漂移，从而保持精确的位姿估计，因此是实现高效SLAM系统的关键。本文针对搭载LiDAR传感器的机器人在室外场景SLAM中提出了一种鲁棒的闭环检测流程。该方法能够适配不同扫描模式、视场角及分辨率的多种LiDAR传感器。它通过LiDAR扫描生成局部地图，并利用地面对齐模块处理LiDAR的平面与非平面运动，确保其在各类平台上的适用性。方法采用保持密度的鸟瞰图投影对这些局部地图进行表征，并提取ORB特征描述子用于位置识别。将特征描述子存储在二叉搜索树中实现高效检索，同时通过自相似性剪枝应对重复环境中的感知混叠问题。在公共数据集和自采集数据集上的大量实验表明，该方法能够实现精确的闭环检测、长期定位及跨平台多地图对齐，且不受LiDAR扫描模式、视场角及运动特征的影响。我们已将流程代码作为开源软件发布于 https://github.com/PRBonn/MapClosures。