arXiv 2503.111452025-07-21cs.RO

利用语义图实现高效且鲁棒的LiDAR SLAM

Leveraging Semantic Graphs for Efficient and Robust LiDAR SLAM

College of Intelligence Science and Technology and the National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, China（智能科学与技术学院和装备状态感知与智能支撑国家重点实验室，国防科技大学，中国）；T Stone Robotics Institute and Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong, China（石墨机器人研究院和机械与自动化工程系，香港中文大学，中国）

Neng Wang, Huimin Lu, Zhiqiang Zheng, Hesheng Wang, Yun-Hui Liu, Xieyuanli Chen

备注：: 8 pages, 4 figures,Accpted for IROS 2025

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AI总结：

本文提出SG-SLAM框架，利用语义图融合几何、语义与拓扑特征，通过双线程架构实现实时鲁棒重定位、回环检测与语义地图构建，在KITTI、MulRAN和Apollo数据集上优于现有方法。

摘要：

准确且鲁棒的同步定位与建图（SLAM）对于自主移动系统至关重要，通常通过利用环境的几何特征来实现。引入语义信息可提供更丰富的场景表示，不仅提升SLAM中的定位精度，还能为下游导航与规划任务提供高级认知功能。现有的逐点语义LiDAR SLAM方法往往效率较低且泛化能力差，使其在多样化的真实场景中鲁棒性不足。本文提出了一种语义图增强的SLAM框架，名为SG-SLAM，有效利用环境结构固有的几何、语义和拓扑特征。语义图作为基础组件，支撑SLAM的关键功能，包括里程计失效时的鲁棒重定位、准确的回环检测以及语义图地图构建。我们的方法采用双线程架构，一个线程专用于在线里程计与重定位，另一个线程处理回环闭合、位姿图优化和地图更新。该设计使我们的方法能够实时运行，并生成全局一致的语义图地图和点云地图。我们在KITTI、MulRAN和Apollo数据集上对所提方法进行了广泛评估，结果表明其优于现有最先进方法。我们的方法已发布在https://github.com/nubot-nudt/SG-SLAM。

英文摘要：

Accurate and robust simultaneous localization and mapping (SLAM) is crucial for autonomous mobile systems, typically achieved by leveraging the geometric features of the environment. Incorporating semantics provides a richer scene representation that not only enhances localization accuracy in SLAM but also enables advanced cognitive functionalities for downstream navigation and planning tasks. Existing point-wise semantic LiDAR SLAM methods often suffer from poor efficiency and generalization, making them less robust in diverse real-world scenarios. In this paper, we propose a semantic graph-enhanced SLAM framework, named SG-SLAM, which effectively leverages the geometric, semantic, and topological characteristics inherent in environmental structures. The semantic graph serves as a fundamental component that facilitates critical functionalities of SLAM, including robust relocalization during odometry failures, accurate loop closing, and semantic graph map construction. Our method employs a dual-threaded architecture, with one thread dedicated to online odometry and relocalization, while the other handles loop closure, pose graph optimization, and map update. This design enables our method to operate in real time and generate globally consistent semantic graph maps and point cloud maps. We extensively evaluate our method across the KITTI, MulRAN, and Apollo datasets, and the results demonstrate its superiority compared to state-of-the-art methods. Our method has been released at https://github.com/nubot-nudt/SG-SLAM.

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。