| 基于无人机自标定的表面流场测量方法 |
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| 引用本文： | 陈诚,王新,李子阳,徐磊,高柱.基于无人机自标定的表面流场测量方法[J].水利水电科技进展,2020,40(4):39-42. |
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| 作者姓名： | 陈诚 王新 李子阳 徐磊 高柱 |
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| 作者单位： | 南京水利科学研究院水文水资源与水利工程科学国家重点实验室,江苏南京210029;河海大学水利水电学院,江苏南京210098;南通大学交通与土木工程学院,江苏南通226019 |
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| 基金项目： | :国家重点研发计划(2017YFC0405703)；国家自然科学基金(51779151)；中央级公益性科研院所基本科研业务费专项(Y220005)；南通市科技计划(JC2018143) |
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| 摘 要： | 针对无人机图像标定及配准等关键技术问题,提出了一种基于无人机自标定的表面流场测量方法。该方法基于运动恢复结构(SFM)三维重建技术完成无人机图像自标定,采用加速稳健特征变换(SURF)方法对无人机图像进行配准。将该方法应用于南京市三汊河河口闸下游表面流场测量,结果表明,基于SFM的三维重建精度可以满足自标定的要求,不需要另外设置地面控制点对无人机图像进行标定,简化无人机标定过程, SURF方法可有效地消除无人机位置飘移对表面流场测量结果的影响。
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| 关 键 词： | 无人机 表面流场 三维重建 自标定 图像配准 SFM技术 SURF方法 |
Surface flow field measurement method based on UAV self-calibration |
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| CHEN Cheng,WANG Xin,LI Ziyang,XU Lei,GAO Zhu.Surface flow field measurement method based on UAV self-calibration[J].Advances in Science and Technology of Water Resources,2020,40(4):39-42. |
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| Authors: | CHEN Cheng WANG Xin LI Ziyang XU Lei GAO Zhu |
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| Affiliation: | State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China;College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China; School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China |
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| Abstract: | Aiming at the key technical problems of unmanned aerial vehicle(UAV)image calibration and registration, a surface flow field measurement method based on UAV self-calibration is proposed, which is based on structure from motion(SFM)3D reconstruction technology to complete the UAV image self-calibration and speed-up robust features(SURF)method for UAV image registration. The method was applied to measure the surface flow field downstream of the Sancha River Estuary Sluice in Nanjing City. The results show that the accuracy of the 3D reconstruction based on SFM can meet the requirements of self-calibration, and there is no need to set ground control points to calibrate the UAV images, which simplifies the UAV calibration process. SURF method can effectively eliminate the influence of UAV position drift on the surface flow field measurement results. |
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| Keywords: | UAV surface flow field 3D reconstruction self-calibration image registration SFM technology SURF method |
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