基于邻域特征点提取和匹配的点云配准

为解决噪声干扰、数据丢失情况下迭代最近点算法的鲁棒性差、配准精度低等问题,提出一种基于邻域特征点提取和匹配的点云配准方法.首先定义一个由点的k邻域曲率、点与邻近点的法向量内积均值以及邻近点与邻域拟合平面的欧氏距离方差等三部分组成的邻域特征参数,结合在移动最小二乘表面构造的曲率特征参数对点云进行两次特征点提取;其次依据直方图特征定义三个匹配条件,并用双重约束获得正确的匹配点对;最后在配准阶段,采用双向构建k维树的迭代最近点算法实现精确配准.实验结果表明,该算法的配准精度较迭代最近点算法提高了90%以上,并且能够在噪声环境下有效地完成缺失点云的配准,在鲁棒性和精确配准方面有明显优势.

Point Cloud Registration Based on Neighborhood Characteristic Point Extraction and Matching

In order to solve the problem of poor robustness and low registration accuracy of the iterative closest point algorithm under noise interference and data loss,a point cloud registration method based on neighborhood characteristic point extraction and matching is proposed.Firstly,a neighborhood characteristic parameter is defined,which is composed of three parts:the k-neighborhood curvature of the point,the normal vector inner product’mean value of the point and the neighborhood points,and the distance variance between the neighborhood points and the neighborhood fitted plane.Neighborhood characteristic parameters and curvature characteristic parameters constructed on moving least square surface are used to extract feature points twice.Secondly,three matching conditions are defined according to the histogram features,and the correct matching point pairs are obtained by double constraints.Finally,in the registration stage,the iterative closest point algorithm of bi-directional k-dimension tree is used to achieve accurate registration.The experimental results show that the registration accuracy of the proposed algorithm is more than 90%higher than that of the iterative closest point algorithm,and it can effectively complete the registration of missing point clouds in noisy environment,which has obvious advantages in robustness and precise registration.