从地下矿井到办公室：一种通用且鲁棒的测距-惯性SLAM框架
From Underground Mines to Offices: A Versatile and Robust Framework for Range-Inertial SLAM
- Instituto Tecnológico de Aragón (ITA)（阿拉贡理工学院）
- Instituto de Investigación en Ingeniería de Aragón (I3A)（阿拉贡工程研究所）
- Universidad de Zaragoza（萨拉戈萨大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出通用鲁棒的测距-惯性SLAM框架LG-SLAM，整合多传感器数据与图优化，适配多场景且调参少，在多样环境下平均误差低于20厘米，性能优于现有先进算法。
AI中文摘要：
同时定位与地图构建（SLAM）是自主机器人应用和自动驾驶汽车的核心组件，使其能够感知环境并在其中作业。过去十年间已有众多SLAM系统被提出，但它们往往难以适配不同场景或传感器配置。本研究提出了LiDAR Graph-SLAM（LG-SLAM），这是一种通用的测距-惯性SLAM框架，仅需极少参数调优即可适配从地下矿井到办公室等多种传感器类型与环境。我们的系统将测距、惯性测量单元与GNSS测量数据整合到基于图的优化框架中，还采用了改进的子地图管理方法和鲁棒的回环检测方法，可有效处理候选回环识别与验证中的不确定性，确保全局一致性与鲁棒性。依托并行架构与GPU集成，系统可实现激光雷达帧率下的位姿估计，同时支持在线回环闭合与图优化。我们通过公开数据集和真实场景数据在多种环境中验证了系统性能，其平均误差始终低于20厘米，性能优于其他现有先进算法。
英文摘要：
Simultaneous Localization and Mapping (SLAM) is an essential component of autonomous robotic applications and self-driving vehicles, enabling them to understand and operate in their environment. Many SLAM systems have been proposed in the last decade, but they are often complex to adapt to different settings or sensor setups. In this work, we present LiDAR Graph-SLAM (LG-SLAM), a versatile range-inertial SLAM framework that can be adapted to different types of sensors and environments, from underground mines to offices with minimal parameter tuning. Our system integrates range, inertial and GNSS measurements into a graph-based optimization framework. We also use a refined submap management approach and a robust loop closure method that effectively accounts for uncertainty in the identification and validation of putative loop closures, ensuring global consistency and robustness. Enabled by a parallelized architecture and GPU integration, our system achieves pose estimation at LiDAR frame rate, along with online loop closing and graph optimization. We validate our system in diverse environments using public datasets and real-world data, consistently achieving an average error below 20 cm and outperforming other state-of-the-art algorithms.
