SGLC：面向LiDAR SLAM的语义图引导的粗-精-细化全回环闭合方法
SGLC: Semantic Graph-Guided Coarse-Fine-Refine Full Loop Closing for LiDAR SLAM
AI总结：
提出SGLC，一种基于语义图引导的粗-精-细化配准的全回环闭合方法，实现LiDAR SLAM中鲁棒的回环检测与6自由度位姿估计，并在多个数据集上超越现有方法。
摘要：
回环闭合是SLAM中的关键组成部分，通过两个主要步骤帮助消除累积误差：回环检测和回环位姿校正。第一步确定是否应执行回环闭合，第二步估计6自由度位姿以校正里程计漂移。当前方法大多专注于开发用于回环闭合检测的鲁棒描述子，往往忽视了回环位姿估计。少数包含位姿估计的方法要么精度较低，要么计算成本高昂。为解决这一问题，我们提出了SGLC，一种实时的语义图引导的全回环闭合方法，具备鲁棒的回环闭合检测和6自由度位姿估计能力。SGLC考虑了前景点和背景点的不同特性。对于前景实例，它构建了一个语义图，该图不仅抽象了点云表示以快速生成描述子和进行匹配，还指导后续的回环验证和初始位姿估计。同时，背景点被利用来为扫描级描述子构建提供更多几何特征，并为进一步的位姿细化提供稳定的平面信息。回环位姿估计采用了一种粗-精-细化的配准方案，同时考虑实例点和背景点的对齐，提供了高效率和准确性。在多个公开数据集上的大量实验证明了其相对于最先进方法的优越性。此外，我们将SGLC集成到SLAM系统中，消除了累积误差并提高了整体SLAM性能。SGLC的实现将在https://github.com/nubot-nudt/SGLC发布。
英文摘要：
Loop closing is a crucial component in SLAM that helps eliminate accumulated errors through two main steps: loop detection and loop pose correction. The first step determines whether loop closing should be performed, while the second estimates the 6-DoF pose to correct odometry drift. Current methods mostly focus on developing robust descriptors for loop closure detection, often neglecting loop pose estimation. A few methods that do include pose estimation either suffer from low accuracy or incur high computational costs. To tackle this problem, we introduce SGLC, a real-time semantic graph-guided full loop closing method, with robust loop closure detection and 6-DoF pose estimation capabilities. SGLC takes into account the distinct characteristics of foreground and background points. For foreground instances, it builds a semantic graph that not only abstracts point cloud representation for fast descriptor generation and matching but also guides the subsequent loop verification and initial pose estimation. Background points, meanwhile, are exploited to provide more geometric features for scan-wise descriptor construction and stable planar information for further pose refinement. Loop pose estimation employs a \mbox{coarse-fine-refine} registration scheme that considers the alignment of both instance points and background points, offering high efficiency and accuracy. Extensive experiments on multiple publicly available datasets demonstrate its superiority over state-of-the-art methods. Additionally, we integrate SGLC into a SLAM system, eliminating accumulated errors and improving overall SLAM performance. The implementation of SGLC will be released at https://github.com/nubot-nudt/SGLC.
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。
