基于运动结构图的无人机序列影像三维重建

doi:10.16182/j.issn1004731x.joss.18-0779

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (6): 1094-1102.doi: 10.16182/j.issn1004731x.joss.18-0779

基于运动结构图的无人机序列影像三维重建

吴宇豪, 曹雪峰, 彭锦超, 徐连瑞

信息工程大学，河南郑州 450000

收稿日期:2018-11-20 修回日期:2019-01-05 发布日期:2020-06-25
作者简介:吴宇豪(1995-)，男，浙江兰溪，硕士生，研究方向为地理空间网格、计算机视觉；曹雪峰(1983-)，男，河北望都，博士，副教授，研究方向为地理空间网格、计算机视觉。
基金资助:
国防科技项目(3601015)，信息工程大学优秀青年基金(2105010205)

3D Reconstruction of UAV Sequence Image Based on Motion Structure Graph

Wu Yuhao, Cao Xuefeng, Peng Jinchao, Xu Lianrui

Information Engineering University, Zhengzhou 450000, China

Received:2018-11-20 Revised:2019-01-05 Published:2020-06-25

摘要/Abstract

摘要： 无人机序列影像具有重叠度高、拓扑联系强的特点。针对现有增量式三维重建算法在处理大规模无人机影像时效率低、输出不稳定的问题，提出基于运动结构图的无人机序列影像三维重建方法。顾及地理位置信息与IMU(Inertial measurement unit) 数据建立图像索引集，提高图像匹配效率，并使用随机抽样一致性算法计算相对运动；使用运动结构图表示影像之间的“ 关联”，采用闭环检测剔除错误边；借助李代数与李群的映射关系，融合1 L 范式与M 估计一次性求解影像全局运动，期间只进行一次光束法平差，降低了计算复杂度。实验验证本文方法在效率和效果上得到了提高。

关键词: 三维重建, 地理位置信息, IMU数据, 运动结构图, 李代数平均

Abstract: UAV sequence image has the characteristics of high overlap and strong topological connection. Aiming at the inefficient and unstable in processing the large-scale UAV images of the existing incremental 3D reconstruction algorithm, a 3D reconstruction method based on motion structure graph is proposed. The image matching index is established by using the geolocation information and IMU data to improve the image matching efficiency, and the random motion consistency algorithm is used to calculate the relative motion; motion structure graph is used to represent the "association" between images, and closed-loop detection algorithm is used to eliminate the false edges. Under the mapping relationship between Lie algebra and Lie group, the global motion of the image is solved at one time by 1 L paradigm and M estimation, and the beam adjustment is used only once. Experiments show that the proposed algorithm improves the efficiency and reconstruction result.

Key words: three-dimensional reconstruction, geographic location information, IMU data, motion structure graph, Lie algebra average

中图分类号:

TP391.9

吴宇豪, 曹雪峰, 彭锦超, 徐连瑞. 基于运动结构图的无人机序列影像三维重建[J]. 系统仿真学报, 2020, 32(6): 1094-1102.

Wu Yuhao, Cao Xuefeng, Peng Jinchao, Xu Lianrui. 3D Reconstruction of UAV Sequence Image Based on Motion Structure Graph[J]. Journal of System Simulation, 2020, 32(6): 1094-1102.

参考文献

[1] Sujit P B, Sousa J, Pereira F L. UAV and AUVs coordination for ocean exploration[C]//Oceans 2009-Europe. IEEE, 2009: 1-7.
[2] Xu Z, Yang J, Peng C, et al. Development of an UAS for post-earthquake disaster surveying and its application in Ms7.0 Lushan Earthquake, Sichuan, China[J]. Computers & Geosciences (S0098-3004), 2014, 68: 22-30.
[3] Watts A C, Ambrosia V G, Hinkley E A. Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use[J]. Remote Sensing (S2072-4292), 2012, 4(6): 1671-1692.
[4] Moulon P, Monasse P, Perrot R, et al. OpenMVG: Open multiple view geometry[C]. International Workshop on Reproducible Research in Pattern Recognition. Springer: Cham, 2016: 60-74.
[5] Mur-Artal R, Tardós J D. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras[J]. IEEE Transactions on Robotics (S1552-3098), 2017, 33(5): 1255-1262.
[6] Zhu Y, Mottaghi R, Kolve E, et al. Target-driven visual navigation in indoor scenes using deep reinforcement learning[C]//2017 IEEE international conference on robotics and automation (ICRA). IEEE, 2017: 3357-3364.
[7] Snavely N, Seitz S M, Szeliski R. Photo tourism: exploring photo collections in 3D[M]//ACM Siggraph 2006 Papers. 2006: 835-846.
[8] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International journal of computer vision (S0920-5691), 2004, 60(2): 91-110.
[9] Cui H, Gao X, Shen S, et al. HSfM: Hybrid structure-from-motion[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 1212-1221.
[10] Havlena M, Torii A, Pajdla T. Efficient structure from motion by graph optimization[C]//European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2010: 100-113.
[11] Hinzmann T, Schönberger J L, Pollefeys M, et al. Mapping on the fly: real-time 3D dense reconstruction, digital surface map and incremental orthomosaic generation for unmanned aerial vehicles[C]//Field and Service Robotics. Springer, Cham, 2018: 383-396.
[12] 许志华, 吴立新, 刘军, 等. 顾及影像拓扑的SfM 算法改进及其在灾场三维重建中的应用[J]. 武汉大学学报(信息科学版), 2015, 40(5): 599-606.
Xu Zhihua, Wu Lixin, Liu Jun, et al. Improvement of SfM algorithm considering image topology and its application in 3D reconstruction of disaster field[J]. Journal of Wuhan University (Information Science Edition), 2015, 40(5): 599-606.
[13] 沈永林, 刘军, 吴立新, 等. 基于无人机影像和飞控数据的灾场重建方法研究[J]. 地理与地理信息科学, 2011, 27(6): 13-17.
Shen Yonglin, Liu Jun, Wu Lixin, et al. Research on disaster site reconstruction method based on UAV image and flight control data [J]. Geographic and Geographic Information Science, 2011, 27(6): 13-17.
[14] Furukawa Y, Ponce J. Dense 3D Motion Capture from Synchronized Video Streams[M]. Springer Berlin Heidelberg: Image and Geometry Processing for 3-D Cinematography, 2010: 1-8.
[15] 刘军, 张永生, 王冬红, 等. PO SAV510-DG 系统外方位元素的计算方法[J]. 测绘技术装备, 2004, 6(4): 6-9.
Liu Jun, Zhang Yongsheng, Wang Donghong, et al. Calculation Method of Outer Orientation Elements in PO SAV510-DG System[J]. Surveying and Mapping Technique, 2004, 6(4): 6-9.
[16] 鲁珊, 雷英杰, 孔韦韦, 等. 基于概率抽样一致性的基础矩阵估计算法[J]. 控制与决策, 2012, 27(3): 425-430.
Lu Shan, Lei Yingjie, Kong Weiwei, et al. Basic matrix estimation algorithm based on probability sampling consistency[J]. Control and Decision, 2012, 27(3): 425-430.
[17] Hall B. Lie groups, Lie algebras, and representations: an elementary introduction[M]. Springer, 2015.
[18] Fletcher P T, Lu C, Joshi S. Statistics of shape via principal geodesic analysis on Lie groups[C]//2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. IEEE, 2003, 1: I-I.
[19] Hartley R, Aftab K, Trumpf J. L1 rotation averaging using the Weiszfeld algorithm[C]//CVPR 2011. IEEE, 2011: 3041-3048.
[20] Hampel F R, Ronchetti E M, Rousseeuw P J, et al. Robust statistics: the approach based on influence functions[M]. John Wiley & Sons, 2011.

基于运动结构图的无人机序列影像三维重建

3D Reconstruction of UAV Sequence Image Based on Motion Structure Graph

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics

本文评价

[1]	陈立家, 王凯, 李世刚, 田延飞. 基于航空影像的航海模拟器视景快速建模方法[J]. 系统仿真学报, 2021, 33(7): 1565-1573.
[2]	康来, 魏迎梅, 蒋杰, 谢毓湘. 融合视惯传感数据的非接触式物体尺寸获取方法[J]. 系统仿真学报, 2020, 32(5): 892-900.
[3]	徐连瑞, 张锦明. 基于Kinect的室内环境建模[J]. 系统仿真学报, 2019, 31(12): 2643-2651.
[4]	张航, 陈彬, 薛含章, 朱正秋, 王戎骁. 基于无人机和LIDAR的三维场景建模研究[J]. 系统仿真学报, 2017, 29(9): 1914-1920.
[5]	林丽萍, 张亚萍. 基于错配剔除的三维重建研究[J]. 系统仿真学报, 2017, 29(11): 2644-2648.
[6]	孟悦, 周明全, 税午阳, 武仲科. 基于单幅图像的三维自由曲面浮雕生成[J]. 系统仿真学报, 2015, 27(12): 3012-3017.
[7]	曹雪峰, 王庆贺, 谢理想. 融合低精度GPS/IMU参数的无人机影像批处理三维重建方法[J]. 系统仿真学报, 2015, 27(10): 2409-2415.
[8]	陈国军, 韦鑫. 基于深度图的可视外壳凹面优化[J]. 系统仿真学报, 2015, 27(10): 2508-2513.