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.

翻译：闭环是SLAM中的关键模块，主要通过回环检测与回环位姿校正两个步骤消除累积误差。第一步决定是否应执行闭环，第二步则估计六自由度位姿以修正里程计漂移。现有方法大多专注于开发鲁棒的回环检测描述子，往往忽略回环位姿估计。少数包含位姿估计的方法或存在精度不足的问题，或计算代价高昂。为解决此问题，本文提出SGLC——一种实时语义图引导的完整闭环方法，具备鲁棒的回环检测与六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：针对前景实例，构建语义图不仅可抽象点云表示以实现快速描述子生成与匹配，还能指导后续的回环验证与初始位姿估计；背景点则被用于提供更多几何特征以构建扫描级描述子，并提供稳定的平面信息以进一步优化位姿。回环位姿估计采用一种考虑实例点与背景点对齐的“粗配准-精配准-优化”配准策略，兼具高效性与高精度。在多个公开数据集上的大量实验证明了其相对于现有先进方法的优越性。此外，我们将SGLC集成至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。