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Abstract
The wind energy sector faces a constant need for annual inspections of wind turbine blades for damage, erosion
and cracks. These inspections are an important part of the wind turbine life cycle and can be very costly and
hazardous to specialists. This has led to the use of automated drone inspections and the need for accurate,
robust and inexpensive systems for localization of drones relative to the wing. Due to the lack of visual
and geometrical features on the wind turbine blade, conventional SLAM algorithms have a limited use. We
propose a cost-effective, easy to implement and extend system for on-site outdoor localization and mapping in
low feature environment using the inexpensive RPLIDAR and an 9-DOF IMU. Our algorithm geometrically
simplifies the wind turbine blade 2D cross-section to an elliptical model and uses it for distance and shape
correction. We show that the proposed algorithm gives localization error between 1 and 20 cm depending on
the position of the LiDAR compared to the blade and a maximum mapping error of 4 cm at distances between
1.5 and 3 meters from the blade. These results are satisfactory for positioning and capturing the overall shape
of the blade.
and cracks. These inspections are an important part of the wind turbine life cycle and can be very costly and
hazardous to specialists. This has led to the use of automated drone inspections and the need for accurate,
robust and inexpensive systems for localization of drones relative to the wing. Due to the lack of visual
and geometrical features on the wind turbine blade, conventional SLAM algorithms have a limited use. We
propose a cost-effective, easy to implement and extend system for on-site outdoor localization and mapping in
low feature environment using the inexpensive RPLIDAR and an 9-DOF IMU. Our algorithm geometrically
simplifies the wind turbine blade 2D cross-section to an elliptical model and uses it for distance and shape
correction. We show that the proposed algorithm gives localization error between 1 and 20 cm depending on
the position of the LiDAR compared to the blade and a maximum mapping error of 4 cm at distances between
1.5 and 3 meters from the blade. These results are satisfactory for positioning and capturing the overall shape
of the blade.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
| Publisher | SCITEPRESS Digital Library |
| Publication date | 2017 |
| ISBN (Print) | 978-989-758-227-1 |
| DOIs | |
| Publication status | Published - 2017 |
| Event | 12th International Conference on Computer Vision Theory and Applications - Porto, Portugal Duration: 27 Feb 2017 → 1 Mar 2017 http://www.visapp.visigrapp.org/ |
Conference
| Conference | 12th International Conference on Computer Vision Theory and Applications |
|---|---|
| Country/Territory | Portugal |
| City | Porto |
| Period | 27/02/2017 → 01/03/2017 |
| Internet address |
Keywords
- Localization
- Mapping
- Scanning
- LiDAR
- IMU
- UAV
- SLAM
- Wind Turbine Blade Inspection
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Dive into the research topics of 'LiDAR-based 2D Localization and Mapping System using Elliptical Distance Correction Models for UAV Wind Turbine Blade Inspection'. Together they form a unique fingerprint.Projects
- 1 Finished
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Leading Edge Roughness - Wind Turbine Blades
Madsen, C. B., Nikolov, I. A. & Ladefoged, K. S.
01/10/2015 → 01/05/2019
Project: Research