一种空间目标三维激光点云与可见光图像重建点云融合方法

A Point Cloud Fusion Method for Space Target 3D Laser Point Cloud and Visible Light Image Reconstruction Method

提出一种高精度的空间目标激光雷达三维扫描点云与可见光图像三维重建点云融合方法,此融合方法利用可见光图像三维重建过程中计算的相机姿态与三维点云模型形心位置优化初值选取,提升了ICP算法的配准精度与配准效率;同时根据两组点云的特点,利用欧式距离阈值对三维点云边缘进行杂点删除,最后利用优化的ICP算法得到带有尺度信息融合的高精度三维重建点云。对空间目标仿真模型进行模拟实验,实验表明本融合方法可有效提升点云密度,填补重建漏洞,提升空间目标三维重建的点云精度。

In this paper,a high-precision spatial target lidar for 3D reconstruction of point cloud and visible light image 3D reconstruction point cloud fusion method is proposed.This fusion method uses the solved 3D reconstruction to invert the camera pose and 3D point cloud model centroid position optimization initial value selection.The registration accuracy and registration efficiency of the ICP algorithm are improved.At the same time,according to the characteristics of the two sets of point clouds,the Euclidean distance threshold is used to delete the noise points of the 3D point cloud edge,and the high-precision 3D reconstruction point with the scale information fusion is obtained.The simulation experiment of the spatial target simulation model shows that the fusion method can effectively improve the point cloud density,fill the reconstruction vulnerability,and improve the point cloud accuracy of the spatial target 3D reconstruction.