基于区域扩张的三维点云模型配准算法

Registration Algorithm of Three-dimensional Point-cloud Models Based on Region Expansion

为提高三维散乱点云自动配准的准确率,提出一种新的基于区域扩张的配准算法。通过局部点云法向量的变化提取特征点,利用区域扩张方法进行初始配准,在搜索精确匹配点的过程中直接剔除错误匹配,使用改进的最近点迭代算法对点云进行精确对齐。实验结果表明,与经典最近迭代点算法和基于曲率的点云自动配准算法相比,该算法能够提升点云配准精度,对特征平滑的点云模型具有较好的效果。

In order to improve the accuracy of automatic registration of three-dimensional scattered point clouds,a new registration algorithm on the basis of region expansion is proposed. Through variations of the normal vector of local pointcloud,characteristic points are extracted. By applying the regional expansion method for initial registration,mismatches can be directly eliminated while searching for accurate matching points. Then,the advanced closest point iterative algorithm is used to accurately align point-clouds. According to experimental results,compared with the classic closest iterative point algorithm and curvature-based point-cloud automatic registration algorithm,this algorithm is capable of improving the accuracy of point cloud registration and has good effects towards the point-cloud model of feature smoothing.