Inside Bridges: Autonomous Crack Inspection with Nano UAVs in GNSS-Denied Environments

Detecting damage to bridges is essential for safety and financial reasons. We focus on inspections inside bridges and claim Unmanned Aerial Vehicles (UAVs) are ideally suited to assist inspections of hard-to-reach areas. Using UAVs in narrow indoor environments imposes constraints on the size of the UAV. While small UAVs are attractive for their agility and reduced risks associated with them, the need to carry cameras and sensors results in a lower bound on thrust and thus UAV and rotor size. Moreover, position and orientation control is particularly demanding inside bridges because satellite navigation is not available. To address these challenges, we implement a nano UAV that is capable of analyzing camera data for cracks with a machine learning model. UAV position is tracked with inertial measurement units, an optical flow, and a laser-based range sensor.

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Publication Details

Published
2024-12-12
DOI
https://doi.org/10.1109/icarcv63323.2024.10821644
Citations
1
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
0.26

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article

Inside Bridges: Autonomous Crack Inspection with Nano UAVs in GNSS-Denied Environments

Patrick Herbers, David Anthony Muller, Markus König, Firdes Çelik et al.
1 citations
Infrastructure Maintenance and Monitoring
0.26
article

Inside Bridges: Autonomous Crack Inspection with Nano UAVs in GNSS-Denied Environments

Patrick Herbers, David Anthony Muller, Markus König, Firdes Çelik, Martin Mönnigmann, Raphael Dyrska
article en
1 citations

Abstract

Detecting damage to bridges is essential for safety and financial reasons. We focus on inspections inside bridges and claim Unmanned Aerial Vehicles (UAVs) are ideally suited to assist inspections of hard-to-reach areas. Using UAVs in narrow indoor environments imposes constraints on the size of the UAV. While small UAVs are attractive for their agility and reduced risks associated with them, the need to carry cameras and sensors results in a lower bound on thrust and thus UAV and rotor size. Moreover, position and orientation control is particularly demanding inside bridges because satellite navigation is not available. To address these challenges, we implement a nano UAV that is capable of analyzing camera data for cracks with a machine learning model. UAV position is tracked with inertial measurement units, an optical flow, and a laser-based range sensor.

Ruhr University Bochum (DE)
European Commission
Openalex Percentile: Top 41%
Infrastructure Maintenance and Monitoring
0.26
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