低空UAV激光点云和序列影像的自动配准方法

doi:10.11947/j.AGCS.2015.20130558

测绘学报 ›› 2015, Vol. 44 ›› Issue (5): 518-525.doi: 10.11947/j.AGCS.2015.20130558

低空UAV激光点云和序列影像的自动配准方法

陈驰^1,2, 杨必胜^1,2, 彭向阳³

1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
2. 武汉大学时空数据智能获取技术与应用教育部工程研究中心, 湖北武汉 430079;
3. 广东电力科学研究院, 广东广州 510080

收稿日期:2013-11-08 修回日期:2014-05-05 出版日期:2015-05-20 发布日期:2015-05-27
通讯作者: 杨必胜 E-mail: bshyang@whu.edu.cn E-mail:bshyang@whu.edu.cn
作者简介:陈驰(1989—),男,博士生, 研究方向为低空摄影测量与激光扫描点云数据处理。E-mail: chenchi_liesmars@foxmail.com
基金资助:
国家自然科学基金(41371431);国家973计划(2012CB725301);教育部博士点基金(20120141110035);南方电网公司重点科技资助(K-GD2013-030)

Automatic Registration of Low Altitude UAV Sequent Images and Laser Point Clouds

CHEN Chi^1,2, YANG Bisheng^1,2, PENG Xiangyang³

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, China;
3. Guangdong Electric Power Research Institute, Guangzhou 510080, China

Received:2013-11-08 Revised:2014-05-05 Online:2015-05-20 Published:2015-05-27
Supported by:
The National Natural Science Foundation of China (No. 41371431);The National Basic Research Program of China(973 Program)(No. 2012CB725301);Doctoral Scientific Fund Project of the Ministry of Education of China(No.20120141110035);Southern Power Grid Company Funded Key Research Program(No.K-GD2013-030)

摘要/Abstract

摘要：

提出了一种低空无人机(unmanned aerial vehicle, UAV)序列影像与激光点云自动配准的方法。首先分别基于多标记点过程与局部显著区域检测对激光点云和序列影像的建筑物顶部轮廓进行提取,并依据反投影临近性匹配提取的顶面特征。然后利用匹配的建筑物角点对,线性解算序列影像外方位元素,再使用建筑物边线对的共面条件进行条件平差获得优化解。最后,为消除错误提取与匹配特征对整体配准结果的影响,使用多视立体密集匹配点集与激光点集进行带相对运动阈值约束的ICP(迭代最临近点)计算,整体优化序列影像外方位元素解。试验结果表明本文方法能实现低空序列影像与激光点云像素级精度的自动配准,联合制作DOM精度满足现行无人机产品1:500比例尺标准。

关键词: 机载激光点云, 序列影像, 点云影像配准, 无人机

Abstract:

It is proposed that a novel registration method for automatic co-registration of unmanned aerial vehicle (UAV) images sequence and laser point clouds. Firstly, contours of building roofs are extracted from the images sequence and laser point clouds using marked point process and local salient region detection, respectively. The contours from each data are matched via back-project proximity. Secondly, the exterior orientations of the images are recovered using a linear solver based on the contours corner pairs followed by a co-planar optimization which is implicated by the matched lines form contours pairs. Finally, the exterior orientation parameters of images are further optimized by matching 3D points generated from images sequence and laser point clouds using an iterative near the point (ICP) algorithm with relative movement threshold constraint. Experiments are undertaken to check the validity and effectiveness of the proposed method. The results show that the proposed method achieves high-precision co-registration of low-altitude UAV image sequence and laser points cloud robustly. The accuracy of the co-produced DOMs meets 1:500 scale standards.

Key words: airborne LiDAR point cloud, UAV image sequences, registration, UAV

中图分类号:

P237

陈驰, 杨必胜, 彭向阳. 低空UAV激光点云和序列影像的自动配准方法[J]. 测绘学报, 2015, 44(5): 518-525.

CHEN Chi, YANG Bisheng, PENG Xiangyang. Automatic Registration of Low Altitude UAV Sequent Images and Laser Point Clouds[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(5): 518-525.

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低空UAV激光点云和序列影像的自动配准方法

Automatic Registration of Low Altitude UAV Sequent Images and Laser Point Clouds

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