像方距离场与物方平面约束联合的航空影像与激光点云精确配准

doi:10.11947/j.AGCS.2025.20240122

测绘学报 ›› 2025, Vol. 54 ›› Issue (1): 64-74.doi: 10.11947/j.AGCS.2025.20240122

• 摄影测量学与遥感 • 上一篇

像方距离场与物方平面约束联合的航空影像与激光点云精确配准

张永军¹^,²(), 朱昌俊¹, 邹思远¹, 刘欣怡¹, 毛庆洲¹, 万一¹^,²()

^1.武汉大学遥感信息工程学院，湖北　武汉　430079
^2.自然资源部粤港澳大湾区自然资源数据协同应用工程技术创新中心，广东　广州　510060

收稿日期:2024-03-28 修回日期:2024-12-10 发布日期:2025-02-17
通讯作者: 万一 E-mail:zhangyj@whu.edu.cn;yi.wan@whu.edu.cn
作者简介:张永军（1975—），男，教授，研究方向为航空航天摄影测量、影像点云融合和三维城市建模。　E-mail：zhangyj@whu.edu.cn
基金资助:
国家自然科学基金(42030102)

Registration of aerial images and LiDAR point clouds based on distance field and plane constraints

Yongjun ZHANG¹^,²(), Changjun ZHU¹, Siyuan ZOU¹, Xinyi LIU¹, Qingzhou MAO¹, Yi WAN¹^,²()

^1.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
^2.Technology Innovation Center for Collaborative Applications of Natural Resources Data in GBA, Ministry of Natural Resources, Guangzhou 510060, China

Received:2024-03-28 Revised:2024-12-10 Published:2025-02-17
Contact: Yi WAN E-mail:zhangyj@whu.edu.cn;yi.wan@whu.edu.cn
About author:ZHANG Yongjun (1975—), male, professor, majors in aerial and space photogrammetry, integration of image and point cloud, and 3D city model reconstruction.　E-mail: zhangyj@whu.edu.cn
Supported by:
The National Natural Science Foundation of China(42030102)

摘要/Abstract

摘要：

在地球观测领域，航空光学影像和机载激光探测与测距（light detection and ranging，LiDAR）点云是获取地表空间信息的主要数据源。精确的几何配准是融合这两类数据的前提。本文提出了一种像方距离场与物方平面约束联合的航空影像与激光点云精确配准方法。该方法分为基于线元距离场的单像配准和线面约束结合的区域网平差两个阶段。在基于线元距离场的单像配准中，首先从航空影像和机载LiDAR点云中分别提取线元素，然后基于航空影像线元素构建距离场，并将点云线基元投影至像平面。通过渐进式稳健求解最小化点云投影线基元在距离场中的全局代价，从而实现单张影像与LiDAR点云的配准。在线面约束结合的区域网平差阶段，选择部分线特征分布较为密集的影像作为关键景影像，并对关键景影像中的同名线元素进行匹配，以提取控制点作为水平及高程约束。此外，还利用影像连接点到最近水平面的距离作为高程约束，通过区域网平差实现多视航空影像与机载点云的配准。试验结果表明，该方法能在初始值较差的情况下实现稳健配准，其配准精度优于点云间距，配准精度与配准效率都显著优于迭代最近点（iterative closest point，ICP）配准算法和通过跨模态匹配进行配准的策略。

关键词: 航空影像, 激光点云, 配准, 距离场, 平面约束

Abstract:

In the field of Earth observation, airborne optical images and airborne light detection and ranging (LiDAR) point clouds are the main data sources for acquiring geo-spatial information. Accurate geometric registration is the prerequisite for the fusion of the two sources of data. In this paper, a modified registration method for airborne LiDAR point clouds and aerial images based on distance field and plane constraints is proposed. This method is divided into two stages: single image registration based on line distance field and bundle block adjustment based on line and plane constraints. In single image registration, line features are extracted from aerial image and airborne LiDAR point cloud respectively, and then distance field is constructed based on the line features of aerial image, and the point cloud line features are projected to the image plane. The global cost of point cloud projection line features in distance field is minimized by progressive robust solution, so that single image can be registered with LiDAR point cloud. In the bundle block adjustment, key frames are selected based on the density of line feature distribution. Subsequently, the features of the conjugate line in key frames are matched to extract the control points as horizontal and elevation constraints. Moreover, the distance between the image tie point and the nearest horizontal plane is used as elevation constraints. The experimental results show that the registration accuracy of the proposed method is close to the average point distance. The proposed method can realize robust registration under the condition of poor initial values, and the registration accuracy is significantly superior to the iterative closest point (ICP) registration method and the registration strategy of cross-modal matching.

Key words: aerial images, LiDAR point clouds, registration, distance field, plane constraints

中图分类号:

P231

张永军, 朱昌俊, 邹思远, 刘欣怡, 毛庆洲, 万一. 像方距离场与物方平面约束联合的航空影像与激光点云精确配准[J]. 测绘学报, 2025, 54(1): 64-74.

Yongjun ZHANG, Changjun ZHU, Siyuan ZOU, Xinyi LIU, Qingzhou MAO, Yi WAN. Registration of aerial images and LiDAR point clouds based on distance field and plane constraints[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(1): 64-74.

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数据集	数据集Ⅰ	数据集Ⅱ
测区位置	浙江省宁波市	武汉大学校园
影像类型	航空多视倾斜影像	航空下视影像
影像数量	1188	50
相机数量	5	1
航高/m	900	250
影像地面分辨率/m	0.051	0.067
LiDAR点云平均点间距/m	0.28	0.20

配准状态	d_XY/m	d_Z/m
配准前	0.75	0.98
DFPC法配准后	0.16	0.19

配准方法	d_XY/m	d_Z/m	配准用时/min	存储开销
HPMEC	0.31	1.04	137	11.9 GB
ICP	0.27	0.32	63	6.2 GB
DFPC	0.06	0.16	12	37 MB

像方距离场与物方平面约束联合的航空影像与激光点云精确配准

Registration of aerial images and LiDAR point clouds based on distance field and plane constraints

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