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Concrete Defects Inspection and 3D Mapping Using City Flyer quadrotor robot

IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 991-1002页

作者： Liang Yang Bing Li Wei Li Howard Brand Biao Jiang Jizhong Xiao the CCNY robotics Lab Electrical Engineering DepartmentCity College of New YorkNY 10031 USA the University of Chinese Academy of Sciences Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110000China the Clemson University SC 29607 USA the Amazon AWS AI SeattleWashington 98170 USA the CCNY robotics Lab Electrical Engineering DepartmentCity College of New YorkNY 10031 the Hostos Community College NY 10451 USA

The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive. Conventional inspection approaches for concrete inspection, using RGB imagebased thresholding methods, are not able to determine metric information as well as accurate location information for assessed defects for conditions. To address this challenge, we propose a deep neural network(DNN) based concrete inspection system using a quadrotor flying robot(referred to as City Flyer) mounted with an RGB-D camera. The inspection system introduces several novel modules. Firstly, a visual-inertial fusion approach is introduced to perform camera and robot positioning and structure 3 D metric reconstruction. The reconstructed map is used to retrieve the location and metric information of the ***, we introduce a DNN model, namely Ada Net, to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface. In order to train the model, we craft a new dataset, i.e., the concrete structure spalling and cracking(CSSC)dataset, which is released publicly to the research ***, we introduce a 3 D semantic mapping method using the annotated framework to reconstruct the concrete structure for visualization. We performed comparative studies and demonstrated that our Ada Net can achieve 8.41% higher detection accuracy than Res Nets and VGGs. Moreover, we conducted five field tests, of which three are manual hand-held tests and two are drone-based field tests. These results indicate that our system is capable of performing metric field inspection,and can serve as an effective tool for civil engineers.

关键词： 3D reconstruction concrete inspection deep neural network quadrotor flying robot visual-inertial fusion

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