- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Theses and Dissertations /
- Point Cloud-based analysis of integrated drone-based...
Open Collections
UBC Theses and Dissertations
UBC Theses and Dissertations
Point Cloud-based analysis of integrated drone-based tracking, mapping, and anomaly detection for GPS-denied environments Alipour, Hedieh
Abstract
In search of a more sustainable future, industries are innovating their asset inspection methods. This thesis explores the integration of robotic, sensing, and artificial intelligence technologies to enhance inspection efficiency. Challenges inherent in traditional inspections, such as limited accessibility, hazardous environments, and human error, have motivated the adoption of automated approaches, specifically within the scope of this thesis, namely, using drones. This study is centered on improving a drone model as known as SKYRON (SK), tailored for conducting industrial inspections, particularly in the oil and gas (O&G) industry. The research starts by highlighting some issues with the SK5 model, such as manual tagging. With the introduction of the SK6 model automated tagging is implemented, while the SK7 model incorporates technologies like Three-Dimension (3D) Light Detection and Ranging (LiDAR) and Simultaneous Localization and Mapping (SLAM), for mapping and corrosion detection. Although these enhancements were primarily designed for the O&G sector their benefits can be extended to industries facing similar inspection challenges.
Item Metadata
| Title |
Point Cloud-based analysis of integrated drone-based tracking, mapping, and anomaly detection for GPS-denied environments
|
| Creator | |
| Supervisor | |
| Publisher |
University of British Columbia
|
| Date Issued |
2024
|
| Description |
In search of a more sustainable future, industries are innovating their asset inspection
methods. This thesis explores the integration of robotic, sensing, and artificial intelligence
technologies to enhance inspection efficiency. Challenges inherent in traditional inspections,
such as limited accessibility, hazardous environments, and human error, have motivated
the adoption of automated approaches, specifically within the scope of this thesis, namely,
using drones.
This study is centered on improving a drone model as known as SKYRON (SK), tailored
for conducting industrial inspections, particularly in the oil and gas (O&G) industry. The
research starts by highlighting some issues with the SK5 model, such as manual tagging.
With the introduction of the SK6 model automated tagging is implemented, while the SK7
model incorporates technologies like Three-Dimension (3D) Light Detection and Ranging
(LiDAR) and Simultaneous Localization and Mapping (SLAM), for mapping and corrosion
detection. Although these enhancements were primarily designed for the O&G sector their
benefits can be extended to industries facing similar inspection challenges.
|
| Genre | |
| Type | |
| Language |
eng
|
| Date Available |
2024-05-23
|
| Provider |
Vancouver : University of British Columbia Library
|
| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
|
| DOI |
10.14288/1.0443758
|
| URI | |
| Degree | |
| Program | |
| Affiliation | |
| Degree Grantor |
University of British Columbia
|
| Graduation Date |
2024-11
|
| Campus | |
| Scholarly Level |
Graduate
|
| Rights URI | |
| Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International