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CHEN Wu, JIANG San, LI Qingquan, JIANG Wanshou. Recent Research of Incremental Structure from Motion for Unmanned Aerial Vehicle Images[J]. Geomatics and Information Science of Wuhan University, 2022, 47(10): 1662-1674. DOI: 10.13203/j.whugis20220130
Citation: CHEN Wu, JIANG San, LI Qingquan, JIANG Wanshou. Recent Research of Incremental Structure from Motion for Unmanned Aerial Vehicle Images[J]. Geomatics and Information Science of Wuhan University, 2022, 47(10): 1662-1674. DOI: 10.13203/j.whugis20220130
shu

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

  • 1.

    Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China

  • 2.

    School of Computer Science, China University of Geosciences(Wuhan), Wuhan 430074, China

  • 3.

    Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China

  • 4.

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Funds: 

The National Natural Science Foundation of China 42001413

the Natural Science Foundation of Hubei Province 2020CFB324

Hong Kong Scholars Program 2021-114

More Information
  • Author Bio:

    CHEN Wu, PhD, professor, specializes in the theoretical and practical research on GNSS positioning and navigation, and SLAM-based 3D reconstruction. E-mail: wu.chen@polyu.edu.hk

  • Corresponding author:

    JIANG San, PhD, associate professor. E-mail: jiangsan@cug.edu.cn

  • Received Date: March 29, 2022
  • Available Online: April 13, 2022
  • Published Date: October 04, 2022
    Graphical Abstract
    • Abstract
  •   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. This paper could give an extensive conclusion and be a useful reference to the related researchers.
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