基于FPFH和法向量的改进点云配准算法

Improved Point Cloud Registration Algorithm Based on FPFH and Normal Vector

摘要:

针对飞行时间法(TOF)获取点云的相关特点,提出一种适用于TOF点云的改进配准算法,首先使用FPFH特征对点云进行粗配准；在精配准阶段,通过法向量夹角特征采样的方法来减少点云的点数,同时又保留点云的关键信息点,并引入KD树和RANSAC方法来改进ICP的配准效率。实验结果表明,该算法具有良好的配准效率和精度,同时具有较大的适用范围。

Abstract:

According to the relevant characteristics of point cloud acquisition of time-of-flight method (TOF), an improved registration algorithm suitable for TOF point cloud was proposed. Firstly, FPFH features were used to perform rough registration of point cloud. And in the fine registration stage, the angle feature sampling method of normal vector was used to reduce the number of points of the point cloud, while retaining the key information points of the point cloud. At the same time, KD tree and RANSAC method were introduced to improve the registration efficiency of ICP. Experimental results show that this method has good registration efficiency and accuracy, and has a wide range of application.