School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Jiangsu Surveying and Mapping Engineering Institute, Nanjing 210013, China.
Sensors (Basel). 2019 Mar 3;19(5):1086. doi: 10.3390/s19051086.
This paper proposes a novel method to achieve the automatic registration of optical images and Light Detection and Ranging (LiDAR) points in urban areas. The whole procedure, which adopts a coarse-to-precise registration strategy, can be summarized as follows: Coarse registration is performed through a conventional point-feature-based method. The points needed can be extracted from both datasets through a matured point extractor, such as the Forster operator, followed by the extraction of straight lines. Considering that lines are mainly from building roof edges in urban scenes, and being aware of their inaccuracy when extracted from an irregularly spaced point cloud, an "infinitesimal feature analysis method" fully utilizing LiDAR scanning characteristics is proposed to refine edge lines. Points which are matched between the image and LiDAR data are then applied as guidance to search for matched lines via the line-point similarity invariant. Finally, a transformation function based on extended collinearity equations is applied to achieve precise registration. The experimental results show that the proposed method outperforms the conventional ones in terms of the registration accuracy and automation level.
本文提出了一种新的方法，以实现城市地区的光学图像和光探测和测距（LiDAR）点的自动配准。整个过程采用粗到精的配准策略，可以总结如下：粗配准通过传统的基于点特征的方法来完成。可以通过成熟的点提取器（例如 Forster 算子）从两个数据集提取所需的点，然后提取直线。考虑到在城市场景中直线主要来自建筑物屋顶边缘，并且从不规则间隔的点云中提取时会出现不准确的情况，因此提出了一种充分利用 LiDAR 扫描特性的“无穷小特征分析方法”来细化边缘线。然后，将图像和 LiDAR 数据之间匹配的点用作通过线-点相似不变量搜索匹配线的指导。最后，应用基于扩展共线方程的变换函数来实现精确配准。实验结果表明，与传统方法相比，该方法在配准精度和自动化程度方面表现更优。
