用点云密度图高效实现多平台激光雷达的回环检测
Efficiently Closing Loops in LiDAR-Based SLAM Using Point Cloud Density Maps
- 基于鸟瞰密度图提取ORB特征，支持不同扫描模式的激光雷达
- 在多个公开和自采数据集上实现高精度回环检测与长期定位
- 代码开源，适合跨平台移动机器人定位与地图构建应用
一致的地图是多数自主移动机器人的关键，通常通过SLAM方法构建。利用位置识别实现回环闭合可缓解全局漂移，从而保持精确的姿态估计，是实现高效SLAM系统的核心。本文提出一种适用于户外激光雷达机器人的鲁棒回环闭合检测流程。该方法兼容不同扫描模式、视场和分辨率的激光雷达传感器。通过从激光雷达扫描生成局部地图，并使用地面对齐模块处理平面与非平面运动，确保跨平台适用性。采用保密度的鸟瞰投影并提取ORB特征描述符进行位置识别，将特征存储于二叉搜索树中实现高效检索，自相似性剪枝策略有效解决重复环境中的感知歧义问题。在公开及自采数据集上的大量实验表明，该方法具备高精度回环检测、长期定位能力以及跨平台多地图对齐能力，且不依赖激光雷达的具体扫描模式、视场或运动轨迹。代码已开源：https://github.com/PRBonn/MapClosures。
原文摘要 · Abstract (English)
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.
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