期刊
AUTOMATION IN CONSTRUCTION
卷 131, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2021.103881
关键词
BIM; Building inspection; GA; UAV
资金
- Foundation for Distinguished Young Talents in Higher Education of Guangdong, China (FDYT) [2020KQNCX060]
- Foundation for Basic and Applied Basic Research of Guangdong Province [2020A1515111189]
This paper introduces an automatic inspection method for building surfaces using UAVs and BIM, aiming to minimize flight length and ensure high-quality image data collection through UAV data collection and path planning, and rapidly calculate optimized flight mission parameters based on the proposed algorithm to enhance building surface inspection automation.
Conventional high-rise building surface inspection is usually inefficient and requires the inspectors to work at heights with high risk. Unmanned aerial vehicles (UAVs) carrying optical or thermal cameras are currently widely utilized as an effective tool for inspection. The UAV-based data collection, especially for unreachable inspection areas, is the basis of unmanned inspection of building surface. In addition, building information modeling (BIM) with rich geometric and semantic information can also be instrumental in building surface in-spection. Therefore, this paper presented an automatic inspection method of building surface, especially for the inspection data collection, by integrating UAV and BIM. To minimize the length of UAV flight while collecting complete and high-quality image data considering the limited endurance capability, the coverage path planning problem is solved using genetic algorithm (GA). The required inspection areas are obtained from the BIM model of the target building to be inspected. To further enhance the automation of building surface inspection, the optimized UAV flight mission parameters are rapidly calculated based on the BIM model and proposed algorithm. A real office building in Shenzhen University campus is used to validate the presented automatic method. The quality of the collected inspection images using the UAV with optimized flight mission are evaluated. The results show that this method leads to time-efficient, accurate, and high-quality inspection data collection for building surface.
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