On-site Semantic Mapping and Waypoint Planning for Autonomous Aerial Bridge Monitoring
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| Publicado en: | ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction vol. 42 (2025), p. 1221-1228 |
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| Otros Autores: | , |
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IAARC Publications
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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MARC
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| 003 | UK-CbPIL | ||
| 035 | |a 3240508633 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 180234 |2 nlm | ||
| 100 | 1 | |a Kim, Yohan |u School of Civil and Environmental Engineering, Yonsei University, Republic of Korea | |
| 245 | 1 | |a On-site Semantic Mapping and Waypoint Planning for Autonomous Aerial Bridge Monitoring | |
| 260 | |b IAARC Publications |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Effective monitoring of aging bridges is critical for ensuring their safety and maintenance. This study introduces a framework for on-site autonomous aerial bridge monitoring using sensor fusion and SLAM (Simultaneous Localization and Mapping). The proposed method utilizes a lightweight LiDAR sensor and a mini PC onboard a drone to generate real-time 3D semantic maps and flight waypoints. YOLOv8-based image segmentation is employed to identify bridge components, achieving a mean Average Precision (mAP50-95) of 86.6% across test data. Segmentation requires less than 10 milliseconds per frame, while processing LiDAR point clouds takes less than 1 second per frame. Waypoint generation based on the semantic map is completed in under 3 seconds. These results demonstrate the framework's capability to deliver precise and reliable on-site monitoring. This system provides a significant advancement in autonomous aerial bridge inspection by enabling efficient and real-time operation. | |
| 653 | |a Onsite | ||
| 653 | |a Simultaneous localization and mapping | ||
| 653 | |a Semantics | ||
| 653 | |a Waypoints | ||
| 653 | |a Real time operation | ||
| 653 | |a Monitoring | ||
| 653 | |a Lidar | ||
| 653 | |a Bridge maintenance | ||
| 653 | |a Image segmentation | ||
| 653 | |a Bridge inspection | ||
| 653 | |a Cameras | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Planning | ||
| 653 | |a Aging | ||
| 653 | |a Calibration | ||
| 653 | |a Sensors | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Automation | ||
| 653 | |a Localization | ||
| 653 | |a Traveling salesman problem | ||
| 653 | |a Robotics | ||
| 700 | 1 | |a Paik, Sunwoong |u School of Civil and Environmental Engineering, Yonsei University, Republic of Korea | |
| 700 | 1 | |a Kim, Hyoungkwan |u School of Civil and Environmental Engineering, Yonsei University, Republic of Korea | |
| 773 | 0 | |t ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction |g vol. 42 (2025), p. 1221-1228 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3240508633/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3240508633/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |