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

  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.