基于RANSAC三维点云的平面提取算法

A planar extraction algorithm based on RANSAC 3D point cloud

针对三维激光扫描点云的表面特征获取时间长,提取效果与减少运行时间不能兼得的问题,本文基于随机抽样一致算法(RANSAC)提出一种自动快速识别点云平面特征的算法。通过对RANSAC算法进行改进,提出基于迭代采样子集判断优化的方法,能够快速准确地识别并提取物体的平面特征。实验结果表明,本算法能有效准确地找到扫描物的平面。

Aiming at the long acquisition time of the planar in 3D laser scanning point cloud and the low accuracy of plane extraction,an algorithm for automatic and rapid recognition of the point cloud plane features based on Random Sample Consensus(RANSAC) is proposed in this paper.By improving the RANSAC method,a method based on iteration and sample points judgment optimization is proposed,which can quickly and accurately identify and extract planar features of object.Experimental results show that the algorithm can find the planar of the object effectively and accurately.