利用点云密度地图高效关闭LiDAR基于SLAM中的环路
Efficiently Closing Loops in LiDAR-Based SLAM Using Point Cloud Density Maps
AI总结：
本文提出了一种鲁棒的环路关闭检测方法，通过生成局部地图并使用地面对齐模块处理平面和非平面运动，实现高效的SLAM系统。
摘要：
一致的地图对于大多数自主移动机器人至关重要，通常使用SLAM方法来构建此类地图。通过地方识别实现的环路关闭有助于通过缓解全局漂移来维持准确的姿态估计，因此是实现有效SLAM系统的关键。本文提出了一种适用于户外SLAM的鲁棒环路关闭检测流程，能够处理不同LiDAR传感器的扫描模式、视野和分辨率。该方法通过LiDAR扫描生成局部地图，并利用地面对齐模块进行对齐，以处理LiDAR的平面和非平面运动，确保在不同平台上的适用性。该方法使用密度保持的鸟瞰图投影生成这些局部地图，并提取ORB特征描述符用于地方识别。它将特征描述符存储在二叉搜索树中以实现高效的检索，并通过自相似性修剪处理重复环境中的感知别名效应。在公开和自记录的数据集上的广泛实验表明，该方法能够实现准确的环路关闭检测、长期定位和跨平台多地图对齐，无论LiDAR的扫描模式、视野和运动特性如何。我们提供该流程的代码作为开源软件，地址为https://github.com/PRBonn/MapClosures。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
