变尺度点云配准算法

Variable Scale Point Cloud Registration Algorithm

针对三维点云配准中点云尺度不一致导致配准精确度不高的问题,提出基于几何重心和质心距离比不变性的多尺度点云配准算法。对点云进行滤波处理;通过点云数据重心与质心建立点云数据之间的尺度比例计算模型;根据配准误差与尺度真值函数关系,对尺度因子进行逐步细化,结合ICP算法进行配准。针对点云数据中不同的情况进行了对比实验,结果表明:在无噪声情况下,实验点云数据配准误差数量级为10^(–12)~10^(–15);在有噪声情况下,实验点云数据配准误差数量级为10^(–4)。

To address the low registration accuracy issue caused by scale mismatch of two point clouds, a multi-scale point cloud registration algorithm is proposed based on the distance ratio invariance of the geometric center of gravity and centroid. The point cloud is firstly filtered. Then, the scale ratio calculation model of the point cloud data is established by computing the point cloud＇s gravity center and centroid. Finally, according to the relationship between the registration error and the scale true value, the scale factor is refined step by step with ICP algorithm. For the noise and the inconsistent point in the point cloud, comparative tests are carried out. The experimental results show that, in the absence of noise, the magnitude of registration error order is 10^（–12）~10^（–15）; in the case with noise, the magnitude of registration error order is 10^（–4）.