Abstract
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.
Highlights
Over the last decade, the public’s awareness of the importance of safety management has been increasing due to a series of large-scale safety accidents
We focus on 3D mapping and a localization method in the context of hierarchical graph-based simultaneous localization and mapping (SLAM) using multiple sensors and the implementations of high-level control of the autonomous flight for the purpose of bridge inspection using a unmanned aerial vehicles (UAVs)
VI odometry is run at 20 Hz, and normal distribution transform (NDT)-based odometry is performed at approximately 3 Hz, while generalized iterative closest point (G-iterative closest point (ICP)) matching is run at 1 Hz to generate global constraints
Summary
The public’s awareness of the importance of safety management has been increasing due to a series of large-scale safety accidents. Preventing the collapse of large structures is crucial, as it causes human harm, and huge economic loss. The importance of structural diagnosis and maintenance to prevent structure collapse and safety accidents is increasing. The number of structures, such as bridges and buildings, is steadily growing both in Korea and abroad, and the number of aging structures more than 30 years old has increased. Existing safety diagnosis and maintenance have limitations. Developing autonomous systems for efficient inspection and maintenance of structures, such as bridges and high-rise buildings, is necessary
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