Concrete Defects Inspection and 3D Mapping Using City Flyer Quadrotor Robot

查看全文 作　　者：Liang [1,2]Yang;Bing [3]Li;Wei [4]Li;Howard [5]Brand;Biao [6]Jiang;Jizhong [5]Xiao

机构地区：[1]the CCNY Robotics Lab,Electrical Engineering Department,City College of New York,NY 10031 USA;[2]the University of Chinese Academy of Sciences,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110000,China;[3]the Clemson University,SC 29607 USA;[4]the Amazon AWS AI,Seattle,Washington 98170 USA;[5]the CCNY Robotics Lab,Electrical Engineering Department,City College of New York,NY 10031;[6]the Hostos Community College,NY 10451 USA

出　　处：《IEEE/CAA Journal of Automatica Sinica》索引2020年第7卷第4期,共12页

基　　金：supported in part by the U.S.National Science Foundation(IIP-1915721);the U.S.Department of Transportation;Office of the Assistant Secretary for Research and Technology(USDOTOST-R)(69A3551747126)through INSPIRE University Transportation Center(http//gffzz9dfe369e9c3a4030hx9u5nnnpf9on65xx.ffgz.tsg.suse.edu.cn)at Missouri University of Science and Technology。 摘　　要：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 defects.Secondly, 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 community.Finally, 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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Concrete Defects Inspection and 3D Mapping Using City Flyer Quadrotor Robot

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