无人机影像增量式运动恢复结构研究进展  

Recent Research of Incremental Structure from Motion for Unmanned Aerial Vehicle Images

作　　者：陈武[1] 姜三 李清泉[3] 江万寿[4] CHEN Wu;JIANG San;LI Qingquan;JIANG Wanshou(Department of Land Surveying and Geo-Informatics,Hong Kong Polytechnic University,Hong Kong 999077,China;School of Computer Science,China University ofGeosciences(Wuhan),Wuhan 430074,China;Shenzhen Key Laboratory of Spatial Smart Sensing and Services,Shenzhen University,Shenzhen 518060,China;State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China)

机构地区：[1]香港理工大学土地测量及地理资讯学系,中国香港999077 [2]中国地质大学(武汉)计算机学院,湖北武汉430074 [3]深圳大学深圳市空间信息智能感知与服务重点实验室,广东深圳518060 [4]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079

出　　处：《武汉大学学报（信息科学版）》2022年第10期1662-1674,共13页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金(42001413);湖北省自然科学基金(2020CFB324);香江学者计划项目(2021-114)。

摘　　要：增量式运动恢复结构(structure from motion, SfM)已经成为无人机影像空中三角测量的常用解决方案。考虑到无人机影像的特点,增量式SfM在效率、精度和稳健性方面的性能有待提高。首先给出了增量式SfM无人机影像空中三角测量的技术流程,然后从特征匹配和几何解算两个方面对其关键技术进行了综述,最后从数据采集方式改变、大场景影像处理、通信与硬件技术发展、深度学习融合等方向,展望了增量式SfM无人机影像空中三角测量的挑战和后续研究,总结本领域的现有研究,为相关研究者提供参考。Objectives: Incremental structure from motion(SfM) has become the widely used workflow for aerial triangulation(AT) of unmanned aerial vehicle(UAV) images. Recently, extensive research has been conducted to improve the efficiency, precision and scalability of SfM-based AT for UAV images. Meanwhile, deep learning-based methods have also been exploited for the geometry processing in the fields of photogrammetry and computer vision, which have been verified with large potential in the AT of UAV images. This paper aims to give a review of recent work in the SfM-based AT for UAV images.Methods:Firstly, the workflow of SfM-based AT is briefly presented in terms of feature matching and geometry solving,in which the former aims to obtain enough and accurate correspondences, and the latter attempts to solve unknown parameters. Secondly, literature review is given for feature matching and geometry solving. For feature matching, classical hand-crafted and recent learning-based methods are presented from the aspects of feature extraction, feature matching and outlier removal. For geometry solving, the principle of SfM-based AT is firstly given with some well-known and widely-used open-source SfM software. Efficiency improvement and large-scale processing are then summarized, which focus on improving the capability of SfM to process large-scale UAV images. Finally, further search is concluded from four aspects, including the change of data acquisition modes, the scalability for large-scale scenes, the development of communication and hardware, and the fusion of deep learning-based methods.Results: The review demonstrates that the existing research promotes the development of SfM-based AT towards the direction of high efficiency, high precision and high robustness, and also promotes the development of both commercial and open-source software packages.Conclusions: Considering the characteristics of UAV images, the efficiency, precision and robustness of SfM-based AT and its application need further improvement and exploitation.

关 键 词：无人机影像 空中三角测量 运动恢复结构 特征匹配 深度学习 

分 类 号：P231[天文地球—摄影测量与遥感]