GNSS约束的长航带无人机影像自检校方法  

GNSS Constrained Self-Calibration for Long Corridor UAV Image

作　　者：黄伟 姜三 刘先铮 江万寿[1,4] HUANG Wei;JIANG San;LIU Xianzheng;JIANG Wanshou(State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430072,China;School of Computer Science,China University of Geosciences(Wuhan),Wuhan 430074,China;Chongqing Xinrong Land Housing Survey Technology Institute Co.Ltd,Chongqing 401120,China;Collaborative Innovation Center of Geospatial Technology,Wuhan 430072,China)

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430072 [2]中国地质大学(武汉)计算机学院,湖北武汉430074 [3]重庆欣荣土地房屋勘测技术研究所有限责任公司,重庆401120 [4]地球空间信息科学协同创新中心,湖北武汉430072

出　　处：《武汉大学学报（信息科学版）》2024年第2期197-207,共11页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金(42001413)。

摘　　要：相机自检校直接决定无人机影像空三的精度。沿输电线路走廊采集的长航带结构无人机影像是一种典型的退化配置,对其自检校容易出现“碗状”效应。为解决该问题,传统方法往往依赖较多控制点,而提出的自检校方法仅需一个控制点。首先研究经典物理模型和最新的数学模型;然后在增量式SfM(structure from motion)框架下,设计了一种联合无人机影像相机检校参数初始化和高精度差分全球导航卫星系统(global navigation satellite system,GNSS)位置信息辅助的相机自检校方法。利用两个实验区域不同采集模式下的4组无人机电力走廊影像进行无控制约束以及单个控制点约束的相机自检校实验。结果表明,提出的相机自检校策略在无控制点约束时,可以有效缓解长航带结构空三的“碗状”效应,减轻模型的弯曲程度,提高自检校空三的绝对精度;单个控制点约束自检校时,水平和高程精度均优于0.06 m。与当前主流开源和商业软件对比,该算法能够得到相当或更高精度。Objectives:Camera self-calibration determines the precision of UAV(unmanned aerial vehicle)image AT(aerial triangulation).The UAV images collected from long transmission line corridors are critical configurations,which may lead to the“bowl effect”with camera self-calibration.To solve such problems,traditional methods rely on more than three GCPs(ground control points),while this study designs a new self-calibration method with only one GCP.Methods:First,two categories camera distortion models,i.e.,physical and mathematical model,are studies in details.Second,within an incremental SfM(structure from motion)framework,a camera self-calibration method is designed,which combines the strategies for initializing camera distortion parameters and fusing high-precision GNSS(global navigation satellite system)observations.Results:The proposed algorithm is verified by using four UAV datasets collected from two sites based on two data acquisition modes.The experimental results show that the proposed method can dramatically alleviate the“bowl effect”and improve the accuracy of AT,and the horizontal and vertical accuracies reach 0.06 m,respectively,when using one GCP.Conclusions:compared with open-source and commercial software,the proposed method achieves competitive or better performance.

关 键 词：相机自检校 无人机影像 数学模型 增量式SfM(structure from motion) GNSS约束 

分 类 号：P237[天文地球—摄影测量与遥感]