地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (9): 1868-1877.doi: 10.12082/dqxxkx.2020.190514

• 遥感科学与应用技术 • 上一篇    下一篇

交叉点结构特征约束的机载LiDAR点云与多视角航空影像配准

王广帅(), 万一, 张永军*()   

  1. 武汉大学遥感信息工程学院,武汉 430079
  • 收稿日期:2019-09-10 修回日期:2019-11-12 出版日期:2020-09-25 发布日期:2020-11-25
  • 通讯作者: 张永军 E-mail:gs_wang@whu.edu.cn;zhangyj@whu.edu.cn
  • 作者简介:王广帅(1994— ),男,山东菏泽人,硕士生,主要从事激光点云与影像配准研究。E-mail:gs_wang@whu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41871368)

Registration of Airborne LiDAR Data and Multi-View Aerial Images Constrained by Junction Structure Features

WANG Guangshuai(), WAN Yi, ZHANG Yongjun*()   

  1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • Received:2019-09-10 Revised:2019-11-12 Online:2020-09-25 Published:2020-11-25
  • Contact: ZHANG Yongjun E-mail:gs_wang@whu.edu.cn;zhangyj@whu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41871368)

摘要:

针对机载点云与航空影像配准精度受点云密度影响较大的问题,本文提出一种交叉点结构特征约束下的机载点云影像配准方法。该方法充分利用激光测距精度较高这一优势,采用点云中的平面结构对影像区域网进行绝对定向约束。首先,利用POS辅助平差后的影像内外方位元素和影像交叉点结构匹配结果,以反投影距离为测度,按照最小二乘准则交会得到交叉点物方结构;然后,在LiDAR点云中自动探测交叉点结构的同名LiDAR平面点;最后,进行交叉点结构特征约束下的航空影像联合区域网平差,得到精确的相机内参数和影像外方位元素。实验结果表明,本文方法在平面和高程方向上均可达到1~2个像素的配准精度。与基于建筑物角特征的配准方法相比,有效克服了点云密度对配准精度的影响,当点云密度较低时,本文方法依然可以取得较高的配准精度。

关键词: 机载LiDAR, 航空影像, 配准, 交叉点结构, 平面特征, 区域网平差

Abstract:

:The integration of Airborne LiDAR data and aerial imagery is useful in data interpretation, land monitoring, and 3D reconstruction. As the first step of these tasks, the geometric registration of the two types of data should be conducted to ensure their alignment. The alignment is sometimes difficult because of differences in their data acquisition mechanisms. The LiDAR data is more reliable and more accurate on smooth surfaces like grounds, walls, and roofs which are difficult to extract from aerial imagery. LiDAR points are mostly sparser than the pixels on aerial images. Considering that the a priori ranging error (1~5 cm) of airborne LiDAR data is usually much smaller than the average point distance (10~50 cm), this paper introduced a plane-constrained block adjustment model to align the two types of data, where the planes were obtained by the intersection of corresponding junction structures. The planar constraints were implemented by forcing surrounding LiDAR points to be on the planes. The proposed block adjustment model is a mixture of the conventional POS-aided and self-calibrated bundle adjustment model and two more types of observing equations. One is the distance between image junction structure observations, and reprojection of the spatial junction structure should be zeros. The other is the normal distance between LiDAR points, and the spatial planes obtained by junction structure should be zeros. In this paper, firstly junction structures in object space were solved based on least squares theory. Then, conjugate planes of junction structures in LiDAR points were detected automatically. Finally, the aerial images block adjustment under constraints of junction structure was performed to obtain the precise interior and exterior orientation parameters. The experimental results showed that both the horizontal and the vertical accuracy of the proposed method could reach 1~2 pixels of the aerial images, which was obviously better than the building-corner-based method. In order to probe into the influence of point cloud density, the LiDAR points were thinned randomly before the geometric registration. The results showed that the accuracy of the proposed method was not influenced but the accuracy of building-corner-based method decreased when the point cloud density decreased, especially the horizontal accuracy. In conclusion, the proposed method takes the advantage of the high-ranging accuracy of LiDAR data to reach high registration accuracy and avoids the influence of the point cloud density. When the density of the LiDAR point cloud is low, a high registration accuracy can be reached using the proposed method.

Key words: Airborne LiDAR, aerial imagery, registration, junction structure, plane feature, block adjustment