基于三维重建的大区域无人机影像全自动拼接方法
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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。
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 3 D 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.
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 3 D 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.
引文
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[2] 张永军.无人驾驶飞艇低空遥感影像的几何处理[J].武汉大学学报(信息科学版),2009,34(3):284-288.
[3] 韩文超,周利剑,贾韶辉,等.基于POS系统的无人机遥感图像融合方法的研究与实现[J].遥感信息,2013,28(3):80-84,90.
[4] 袁修孝.POS辅助光束法区域网平差[J].测绘学报,2008,37(3):342-348.
[5] 孙家抦.遥感原理与应用[M].武汉:武汉大学出版社,2013.
[6] AGARWAL S,FURUKAWA Y,SNAVELY N,et al.Building Rome in a day[J].Communications of the ACM,2011,54(10):105-112.
[7] LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[8] 张剑清,潘励,王树根.摄影测量学[M].2版.武汉:武汉大学出版社,2009.
[9] WU C.SiftGPU:a GPU implementation of scale invariant feature transform[EB/OL].[2018-02-12].http://cs.unc.edu/～ccwu/siftgpu.
[10] ZIEGLER G,TEVS A,THEOBALT C,et al.GPU point list generation through histogram pyramids[M].[S.1.]:Max Planck Society,2006.
[11] 柯涛,田一涵,段延松.基于GPU的 SIFT 特征点快速匹配[J].测绘与空间地理信息,2014,37(12):14-16.
[12] 胡庆武,艾明耀,殷万玲,等.大旋角无人机影像全自动拼接方法研究[J].计算机工程,2012,38(15):152-155.
[13] TRIGGS B,MCLAUCHLAN P F,HARTLEY R I,et al.Bundle adjustment——a modern synthesis[C]//Proceedings of International Workshop on Vision Algorithms.Berlin,Germany:Springer,1999:298-372.
[14] WU C,AGARWAL S,CURLESS B,et al.Multicore bundle adjustment[C]//Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2011:3057-3064.
[15] ARA S,MOUNT D M,NETANYAHU N S,et al.An optimal algorithm for approximate nearest neighbor searching fixed dimensions[J].Journal of the ACM,1998,45(6):891-923.
[16] FURUKAWA Y,PONCE J.Accurate,dense,and robust multiview stereopsis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(8):1362-1376.
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