为提高三维点云数据配准精度和速度，提出一种基于K‐D树优化的IC P三维点云配准方法，首先采用中心重合法实现点云数据的粗配准，然后利用K‐D tree快速搜索最近点对改进传统ICP方法，完成三维点云数据精配准，该方法克服传统IC P算法中由于利用欧式距离来判断最近点所引起的工作量大、耗费时间多的缺陷，提高点云的配准速度。在此基础上利用斯坦福不同密度Bunny点云数据进行实验验证，结果表明在采用中心重合法实现三维点云粗配准的基础上，利用K‐D tree优化ICP算法，能够提高点云配准的精度、速度和稳定性。%In order to improve the precision and speed of the three‐dimensional point cloud registration ,it presents a three dimensional point cloud registration based on ICP K‐D tree optimization in this paper .First of all ,the centre superposition method is adopted to realize the point cloud coarse registration ,and then improve the traditional ICP w here the K‐D tree is used to quickly search the closest pair of points to enhance the speed of the point cloud registration .Finally the three dimensional point cloud coarse registration is completed precisely .The method overcomes the defects of the traditional ICP algorithm using Euclidean distance to determine the closest pair of points w hich is time‐consuming and plains lots of work .On the basis of this method ,the experiment can be verified through different density Bunny Stanford point cloud data .The result shows that using K‐d tree optimization of ICP algorithm ,the precision ,speed and stability of the point cloud registration are improved w hen the centre superposition method is adopted to realize the three dimensional point cloud coarse registration .
展开▼
