低空影像SfM三维重建的耦合单-多旋转平均迭代优化法

doi:10.11947/j.AGCS.2019.20180063

测绘学报 ›› 2019, Vol. 48 ›› Issue (6): 688-697.doi: 10.11947/j.AGCS.2019.20180063

低空影像SfM三维重建的耦合单-多旋转平均迭代优化法

何海清^1,2, 陈敏³, 陈婷⁴, 李大军^1,2, 陈晓勇^1,2

1. 东华理工大学测绘工程学院, 江西南昌 330013;
2. 流域生态与地理环境监测国家测绘地理信息局重点实验室, 江西南昌 330013;
3. 西南交通大学地球科学与环境工程学院, 四川成都 611756;
4. 东华理工大学水资源与环境工程学院, 江西南昌 330013

收稿日期:2018-02-12 修回日期:2018-09-25 出版日期:2019-06-20 发布日期:2019-07-09
作者简介:何海清(1983-),男,博士后,副教授,研究方向为数字摄影测量与遥感。E-mail:hyhqing@163.com
基金资助:
国家自然科学基金（41861062；41401526；41501492）；江西省自然科学基金（20171BAB213025；20181BAB203022）；江西省高等学校科技落地计划（KJLD14049）

Single and multiple rotation averaging iterative optimization coupled 3D reconstruction for low-altitude images using SfM algorithm

HE Haiqing^1,2, CHEN Min³, CHEN Ting⁴, LI Dajun^1,2, CHEN Xiaoyong^1,2

1. School of Geomatics, East China University of Technology, Nanchang 330013, China;
2. Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, Nanchang 330013, China;
3. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;
4. School of Water Resources & Environmental Engineering, East China University of Technology, Nanchang 330013, China

Received:2018-02-12 Revised:2018-09-25 Online:2019-06-20 Published:2019-07-09
Supported by:
The National Natural Science Foundation of China (Nos. 41861062;41401526;41501492);The Jiangxi Natural Science Foundation of China (Nos. 20171BAB213025;20181BAB203022);The Higher School Science and Technology Landing Project of Jiangxi Province (No. KJLD14049)

摘要/Abstract

摘要： 针对现有SfM算法初始模型构建、关联影像递增不尽合理导致误差累积等问题，提出一种耦合单-多旋转平均迭代优化的低空影像SfM三维重建方法。首先，基于影像相对定向关系建立初始影像关联无向图，以多因素为依据构建最优增量决策函数锁定强关联影像。其次，利用顾及粗差的单旋转平均递增式添加强关联影像以扩充局部参考系下影像关联有向图，并采用四元数支持下无须物方点参与平差的多旋转平均迭代优化计算全局一致性旋转与平移矩阵最优解。最后，利用光束法区域网平差统一优化整个网络，得到精确的旋转与平移矩阵及物方点。试验结果表明，与现有低空影像旋转平均方法相比，本文方法能解算出更为精确的外方位元素和恢复出更为密集的三维物方点云，且相对于增量式SfM算法效率提升显著。

关键词: 增量式SfM, 旋转平均, 增量决策函数, 四元数, 外方位元素

Abstract: To reduce the cumulative errors caused by initial stereo model construction and related images augmentation in the existing structure from motion based methods, a single & multiple rotation averaging iterative optimization coupled 3D reconstruction method for low-altitude images is proposed. Firstly, an initial undirected graph of correlated images is established based on images relative orientation. Strongly correlated images are found in terms of the optimal incremental decision function, which is constructed based on multiple factors. Secondly, single rotation averaging considering gross erroris used to expand images directed graph in local reference framework, the global uniform optimal solution of rotation and translation matrices can be computed by quaternion supported multiple rotation averaging without involving 3D points. Finally, bundle adjustment is used to refine 3D points, rotation and translation matrices.The experimental results show that the proposed method can achieve more accurate exterior orientation elements and recover more 3D object points, and significantly improve the efficiency of the incremental structure from motion.

Key words: incremental structure from motion, rotation averaging, incremental decision function, quaternion, exterior orientation elements

中图分类号:

P231

何海清, 陈敏, 陈婷, 李大军, 陈晓勇. 低空影像SfM三维重建的耦合单-多旋转平均迭代优化法[J]. 测绘学报, 2019, 48(6): 688-697.

HE Haiqing, CHEN Min, CHEN Ting, LI Dajun, CHEN Xiaoyong. Single and multiple rotation averaging iterative optimization coupled 3D reconstruction for low-altitude images using SfM algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(6): 688-697.

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低空影像SfM三维重建的耦合单-多旋转平均迭代优化法

Single and multiple rotation averaging iterative optimization coupled 3D reconstruction for low-altitude images using SfM algorithm

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