With the increasing demand for autonomous systems in the field of inspection, the use of unmanned aerial vehicles (UAVs) to replace human labor is becoming more frequent. However, the Global Positioning System (GPS) signal is usually denied in environments near or under bridges, which makes the manual operation of a UAV difficult and unreliable in these areas. This paper addresses a novel hierarchical graph-based simultaneous localization and mapping (SLAM) method for fully autonomous bridge inspection using an aerial vehicle, as well as a technical method for UAV control for actually conducting bridge inspections. Due to the harsh environment involved and the corresponding limitations on GPS usage, a graph-based SLAM approach using a tilted 3D LiDAR (Light Detection and Ranging) and a monocular camera to localize the UAV and map the target bridge is proposed. Each visual-inertial state estimate and the corresponding LiDAR sweep are combined into a single subnode. These subnodes make up a “supernode” that consists of state estimations and accumulated scan data for robust and stable node generation in graph SLAM. The constraints are generated from LiDAR data using the normal distribution transform (NDT) and generalized iterative closest point (G-ICP) matching. The feasibility of the proposed method was verified on two different types of bridges: on the ground and offshore.

中文翻译：

---

基于HG-SLAM的无人机飞行桥梁检测：基于分层图的SLAM

随着检查领域对自动系统的需求不断增加，无人驾驶飞机（UAV）取代人工的使用变得越来越普遍。但是，通常在桥梁附近或桥梁下方的环境中会拒绝全球定位系统（GPS）信号，这使得无人机在这些区域的手动操作变得困难且不可靠。本文介绍了一种新颖的基于分层图的同时定位和地图绘制（SLAM）方法，用于使用飞行器进行全自动桥梁检查，以及用于实际进行桥梁检查的UAV控制的技术方法。由于所涉及的恶劣环境以及GPS使用的相应限制，提出了一种基于图的SLAM方法，该方法使用倾斜的3D LiDAR（光检测和测距）和单眼相机来定位无人机并映射目标桥梁。每个视觉惯性状态估计值和对应的LiDAR扫描都合并为一个子节点。这些子节点组成一个“超级节点”，该超级节点由状态估计和累积的扫描数据组成，用于在图SLAM中生成健壮且稳定的节点。使用正态分布变换（NDT）和广义迭代最近点（G-ICP）匹配从LiDAR数据生成约束。在两种不同类型的桥梁上：地面和海上桥梁，都验证了该方法的可行性。这些子节点组成一个“超级节点”，该超级节点由状态估计和累积的扫描数据组成，用于在图SLAM中生成健壮且稳定的节点。使用正态分布变换（NDT）和广义迭代最近点（G-ICP）匹配从LiDAR数据生成约束。在两种不同类型的桥梁上：地面和海上桥梁，都验证了该方法的可行性。这些子节点组成一个“超级节点”，该超级节点由状态估计和累积的扫描数据组成，用于在图SLAM中生成健壮且稳定的节点。使用正态分布变换（NDT）和广义迭代最近点（G-ICP）匹配从LiDAR数据生成约束。在两种不同类型的桥梁上：地面和海上桥梁，都验证了该方法的可行性。