融合反射强度图像的地铁隧道点云自动配准

针对地铁隧道点云数据特征点少、在大视角点云数据间配准拼接时出现精度差、效率低等问题,本文以提高配准效率及精度作为出发点,以目前主流的ICP算法为基础,首先将激光点云按中心投影方式生成反射强度图像并以此作为配准源,采用规则格网分割提取匹配,建立均匀分布的同名点;然后利用反射强度图像上的同名点与点云之间的一一对应关系,完成视角点云间的初配准;最后在初次配准的基础上,采用KD树改进算法进行点云数据的精细配准。试验结果表明,本文在实现点云数据自动配准的同时,提高了地铁隧道点云数据的配准效率及精度。

Automatic point cloud registration method of subway tunnels based on reflection intensity images

Aiming at the problems of low accuracy and low efficiency in registration and splicing of subway tunnel point cloud data, the characteristic points are seldom in the large perspective point cloud data. In this paper, starting from improving the registration efficiency and accuracy and based on the current mainstream ICP algorithm, the laser point cloud is generated firstly by the reflection intensity image in the way of central projection and then used as the registration source. Regular grid segmentation is adopted to extract matching and establish uniform distribution of the same name points. Then the one-to-one correspondence between the point of the same name and the point cloud on the reflection intensity image which is used to complete the initial registration among the point clouds of the angle of view. Finally, on the basis of the initial registration, KD tree improved algorithm is adopted to carry out the fine registration of point cloud data. The experimental results show that this paper not only realizes the automatic registration of point cloud data, but also improves the registration efficiency and accuracy of subway tunnel point cloud data.