计算机工程 ›› 2019, Vol. 45 ›› Issue (4): 235-240.doi: 10.19678/j.issn.1000-3428.0049800

• 图形图像处理 • 上一篇    下一篇

基于三维重建的大区域无人机影像全自动拼接方法

邹松1,2,唐娉1,2,胡昌苗1,2,单小军1,2   

  1. 1.中国科学院遥感与数字地球研究所,北京 100094; 2.中国科学院大学,北京100049
  • 收稿日期:2017-12-21 出版日期:2019-04-15 发布日期:2019-04-15
  • 作者简介:邹松(1993—),男,硕士研究生,主研方向为遥感图像处理;唐娉(通信作者),研究员、博士生导师;胡昌苗、单小军,助理研究员。
  • 基金项目:

    国家自然科学基金青年基金(41601384)。

Full-automatic Splicing Method of Unmanned Aerial Vehicle Images in Large Area Based on 3D Reconstruction

ZOU Song1,2,TANG Ping1,2,HU Changmiao1,2,SHAN Xiaojun1,2   

  1. 1.Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100094,China; 2.University of Chinese Academy of Sciences,Beijing 100049,China
  • Received:2017-12-21 Online:2019-04-15 Published:2019-04-15

摘要:

低成本无人机由于不具有精密的惯性导航系统,其拍摄区域有可能是一些难以布设控制点的无人区,因此无法采用传统的航空摄影测量处理手段获得拍摄区域的拼接影像。为此,提出一种基于三维重建的无人机影像全自动拼接方法。利用从运动恢复结构和多视角立体算法重建拍摄区域的密集点云,根据密集点云数据采用一种基于邻点分布约束的点坐标插值算法内插待定点空间坐标,运用间接微分纠正方法对影像进行几何校正,从而获得几何一致的拼接影像。实验结果表明,该方法全程无需人工干预且拼接耗时短,相邻影像之间几何拼接精度约为2.5个像素。

关键词: 无人机, 影像拼接, 三维重建, 从运动恢复结构, 多视角立体

Abstract:

Because the low-cost Unmanned Aerial Vehicle(UAV) does not have a sophisticated inertial navigation system,the shooting area may be some unmanned areas where it is difficult to lay out control points,the conventional aerial photogrammetric processing method cannot be used to obtain the splicing image of the shooting area.Therefore,a method of full-automatic splicing images based on 3D reconstruction is proposed.Dense point cloud of the shooting area is generated by using Structure From Motion(SFM) and Multi-view Stereo(MVS) algorithms,a point coordinate interpolation algorithm based on adjacent point distribution constraint is used to interpolate the space coordinates to be undetermined,and images are geometric-rectified by using indirect differential correction algorithm,so a geometric-consistent splicing image is obtained.Experimental result shows that the method requires no human intervention and the process takes a short time,and the geometric splicing accuracy between adjacent images is about 2.5 pixels.

Key words: Unmanned Aerial Vehicle(UAV), image splicing, 3D reconstruction, Structure From Motion(SFM), Multi-view Stereo(MVS)

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