基于智能手机APP的非合作目标三维重建与交互
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摘要
基于"软件定义卫星"的思想,设计并实现了一套利用天基观测序列对非合作目标进行三维重建的软件系统。考虑卫星与地面用户的交互性,该软件系统由2部分组成:星上云节点软件以及地面用户软件。在该软件系统中,普通用户可以通过地面用户软件,实时观测太空中非合作目标的三维结构,加强对太空的了解;对科研人员而言,对非合作目标的三维重建是对非合作目标进行抓取、捕获、提供在轨服务等进一步研究的基础。为了满足对非合作目标定轨、定姿的进一步需求,软件系统提供了三维重建中生成的匹配特征点的位置信息以及非合作目标与摄像机本体之间的向量信息。针对利用运动恢复结构(SFM)恢复三维结构存在的点云稀疏、可视化效果差的问题,采用SFM稀疏重建获得点云的基础上,进行泊松表面分布重建,获得致密、均匀的网格表面。由于缺乏空间非合作目标成像数据,采用地面仿真数据进行实验,结果表明,使用该方法可以完成对非合作目标的三维重建,重建效果好,并且三维重建中获得的匹配特征点数据可以对非合作目标的定姿、定轨提供数据支持。
In this paper,a software system is designed and implemented,which uses space-based observation sequence to reconstruct the 3 D non-cooperative target based on the thought of software defined satellites. Considering the interaction between the satellite and the ground users,the system consists of two parts:software on the satellite cloud node and an APP on the ground users. In this system,normal users can watch the 3 D structure of the space targets in real time and improve their knowledge of the space. For researchers,the 3 D reconstruction of non-cooperative targets is the basis for further research such as capturing and providing on-orbit services. In order to meet the further requirements of non-cooperative target orbit and pose determination,the system provides the position information of matching feature points generated in 3 D reconstruction and the vector information between non-cooperative target and camera ontology. In order to solve the problem of sparse point clouds and poor visualization in 3 D reconstruction using structure from motion( SFM),Poisson surface distribution reconstruction is carried out on the basis of sparse point cloud reconstruction using SFM to obtain dense and uniform grid surface. Owing to the lack of the non-cooperative space target imaging data,the ground imaging simulation is carried out to verify the algorithm. The results show that this method can be used to reconstruct the 3 D non-cooperative target and the reconstruction is accurate. Meanwhile,the recovery 3 D point cloud can be used to determine the orbit and attitude of the non-cooperative space target.
In this paper,a software system is designed and implemented,which uses space-based observation sequence to reconstruct the 3 D non-cooperative target based on the thought of software defined satellites. Considering the interaction between the satellite and the ground users,the system consists of two parts:software on the satellite cloud node and an APP on the ground users. In this system,normal users can watch the 3 D structure of the space targets in real time and improve their knowledge of the space. For researchers,the 3 D reconstruction of non-cooperative targets is the basis for further research such as capturing and providing on-orbit services. In order to meet the further requirements of non-cooperative target orbit and pose determination,the system provides the position information of matching feature points generated in 3 D reconstruction and the vector information between non-cooperative target and camera ontology. In order to solve the problem of sparse point clouds and poor visualization in 3 D reconstruction using structure from motion( SFM),Poisson surface distribution reconstruction is carried out on the basis of sparse point cloud reconstruction using SFM to obtain dense and uniform grid surface. Owing to the lack of the non-cooperative space target imaging data,the ground imaging simulation is carried out to verify the algorithm. The results show that this method can be used to reconstruct the 3 D non-cooperative target and the reconstruction is accurate. Meanwhile,the recovery 3 D point cloud can be used to determine the orbit and attitude of the non-cooperative space target.
引文
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[2]NISTER D.Calibration with robust use of cheirality by quasi-affine reconstruction of the set of camera projection centres[C]∥8th IEEE International Conference on Computer Vision(ICCV2001).Piscataway,NJ:IEEE Press,2001:116-123.
[3]NISTER D.Frame decimation for structure and motion[C]∥2nd European Workshop on 3D Structure from Multiple Images of Large-Scale Environments.Berlin:Springer,2000:17-34.
[4]TRIGGS B,MCLAUNCHLAN P F,HARTLEY R I,et al.Bundle adjustment-A modern synthesis[C]∥International Workshop on Vision Algorithms.Berlin:Springer,1999:298-372.
[5]LOURAKIS M I,ARGYROS A A.SBA:A software package for generic sparse bundle adjustment[J].ACM Transactions on Mathematical Software(TOMS),2009,36(1):1-30.
[6]FURUKAWA Y,PONCE J.Accurate,dense,and robust multiview stereopsis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(8):1362-1376.
[7]唐永鹤.基于序列图像的空间非合作目标三维重建关键技术研究[D].长沙:国防科学技术大学,2012:13-36.TANG Y H.Researches on 3D reconstruction technologies for non-cooperative space targets using image sequences[D].Changsha:National University of Defense Technology,2012:13-36(in Chinese).
[8]张浩鹏,魏全茂,张威,等.基于序列图像的空间目标三维重建[J].北京航空航天大学学报,2016,42(2):273-279.ZHANG H P,WEI Q M,ZHANG W,et al.Sequential-imagebased space object 3D reconstruction[J].Journal of Beijing University of Aeronautics and Astronautics,2016,42(2):273-279(in Chinese).
[9]何豫航,岳俊.基于CMVS/PMVS多视角密集匹配方法的研究与实现[J].测绘地理信息,2013,38(3):20-23.HE Y H,YUE J.Research and implementation based on multiview dense matching by applying CMVS/PMVS[J].Journal of Geomatics,2013,38(3):20-23(in Chinese).
[10]曾蔚,王汇源,刘莹奇,等.基于IR-SFS算法空间目标红外影像3D重建[J].中国光学,2014,7(3):376-388.ZENG W,WANG H Y,LIU Y Q,et al.3D reconstruction of space target IR image based on IR-SFS algorithm[J].Chinese Optics,2014,7(3):376-388(in Chinese).
[11]曹彩秀.非合作航天器位姿在轨测量方法的研究[D].北京:北京邮电大学,2015:25-34.CAO C X.Pose on-orbit measurement of non-cooperative spacescraft[D].Beijing:Beijing University of Posts and Telecommunications,2015:25-34(in Chinese).
[12]郑恩,成耀天,林靖宇.采用去抖动模糊算法的稠密三维重建[J].计算机工程与应用,2018,54(1):217-223.ZHENG E,CHENG Y T,LIN J Y.Dense 3D reconstruction based on removing camera-shake fuzzy algorithm[J].Computer Engineering and Applications,2018,54(1):217-223(in Chinese).
[13]VALENTIN J P C,SENGUPTA S,WARRELL J,et al.Mesh based semantic modelling for indoor and outdoor scenes[C]∥IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE Press,2013:2067-2074.
[14]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[15]FORSYTH D.Computer vision:A modern approach[J].Prentice Hall Professional Technical Reference,2002,14(1):294-299.
[16]BOLITHO M,KAZHDAN M M,BURNS R C,et al.Parallel Poisson surface reconstruction[C]∥International Symposium on Visual Computing.Berlin:Springer,2009:678-689.