Bridge Inspection Using Unmanned Aerial Vehicle Based on HG-SLAM: Hierarchical Graph-based SLAM

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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.
Publisher
MDPI
Issue Date
2020-09
Language
English
Article Type
Article
Citation

REMOTE SENSING, v.12, no.18, pp.3022

ISSN
2072-4292
DOI
10.3390/rs12183022
URI
http://hdl.handle.net/10203/276634
Appears in Collection
EE-Journal Papers(저널논문)
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