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 coarse-fine-refine registration scheme that considers the alignment of both instance points and background points, offering high efficiency and accuracy. We evaluate the loop closing performance of SGLC through extensive experiments on the KITTI and KITTI-360 datasets, demonstrating its superiority over existing 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.

翻译：闭环检测是SLAM中的关键组成部分，它通过两个主要步骤来消除累积误差：闭环检测与闭环位姿校正。第一步决定是否应执行闭环，第二步则估计6自由度位姿以校正里程计漂移。现有方法大多专注于开发鲁棒的闭环检测描述子，往往忽视了闭环位姿估计。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。为解决此问题，我们提出了SGLC，一种实时语义图引导的全闭环方法，具备鲁棒的闭环检测与6自由度位姿估计能力。SGLC考虑了前景点与背景点的不同特性。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实现快速描述子生成与匹配，还指导后续的闭环验证与初始位姿估计。同时，背景点被用于为逐帧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。闭环位姿估计采用了一种粗-精-细化配准方案，该方案同时考虑了实例点与背景点的对齐，兼具高效性与高精度。我们在KITTI和KITTI-360数据集上通过大量实验评估了SGLC的闭环性能，证明了其优于现有先进方法。此外，我们将SGLC集成到一个SLAM系统中，有效消除了累积误差并提升了整体SLAM性能。SGLC的实现代码将在https://github.com/nubot-nudt/SGLC发布。