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On-site Semantic Mapping and Waypoint Planning for Autonomous Aerial Bridge Monitoring

Tallennettuna:
Julkaisussa:ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction vol. 42 (2025), p. 1221-1228
Päätekijä: Kim, Yohan
Muut tekijät: Paik, Sunwoong, Kim, Hyoungkwan
Julkaistu:
IAARC Publications
Aiheet:
Onsite
Simultaneous localization and mapping
Semantics
Waypoints
Real time operation
Monitoring
Lidar
Bridge maintenance
Image segmentation
Bridge inspection
Cameras
Accuracy
Deep learning
Planning
Aging
Calibration
Sensors
Methods
Algorithms
Automation
Localization
Traveling salesman problem
Robotics
Linkit:Citation/Abstract
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Abstrakti: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.
Lähde:Advanced Technologies & Aerospace Database