An integrated system design for building inspection by autonomous UAVs

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2017

Author

Küçüksubaşı, Fatih

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Inspection of buildings throughout their lifecycle is vital in terms of human safety as the number of structures increases expeditiously. However, it is not easy to perform inspections for all cases. Physical reachability and complexity of the buildings are major problems along with the safety of inspectors during on-site operations. In this context, Unmanned Aerial Vehicles (UAV) have recently shown great performance collecting visual data through autonomous exploration and mapping in building inspection. Yet, the number of studies is limited considering the post processing of the data and its integration with autonomous UAVs. These will enable huge steps onward into full automation of building inspection. In this regard, this work presents a decision making tool for revisiting tasks in visual building inspection by autonomous UAVs. The tool is an implementation of fine-tuning a pretrained Convolutional Neural Network for surface crack detection. It offers an optional mechanism for task planning of revisiting pinpoint locations during inspection. It is integrated to a quadrotor UAV system that can autonomously navigate in GPSdenied environments. The UAV is equipped with onboard sensors and computers for autonomous localization, mapping and motion planning. Additionally, a Graphical User Interface is developed in order to wrap the high-level features of the system for users. The integrated system is tested through simulations and real-world experiments. The results show that the system achieves crack detection and autonomous navigation in GPS-denied environments for building inspection.

Subject Keywords

Drone aircraft, Building inspection, Navigation (Aeronautics)

URI

http://etd.lib.metu.edu.tr/upload/12621788/index.pdf
https://hdl.handle.net/11511/27066

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

F. Küçüksubaşı, “An integrated system design for building inspection by autonomous UAVs,” M.S. - Master of Science, Middle East Technical University, 2017.