基于邻域表面形变信息加权的点云配准

Point Cloud Registration Based on Weighting Information of Neighborhood Surface Deformation

为了提高点云的配准精度,解决单一特征导致迭代最近点(ICP)算法在噪声干扰和数据缺失的情况下鲁棒性差的问题,提出一种基于邻域表面形变信息加权的点云配准方法。先为简化点的邻域信息提出以邻近点数量为约束的邻域构建方法,考虑邻近点对采样点的影响并引入加权方法提高内部形态描述子(ISS)特征点提取算法的提取效率;计算邻域的法向量内积均值对点云进行第二次特征点提取;再利用快速点特征直方图(FPFH)进行特征描述,并运用双重约束条件确定匹配点对关系;最后在配准阶段,采用双向k维树ICP(DTICP)算法来实现精确配准。实验结果表明,与经典ICP算法相比,所提方法能够在噪声环境下有效配准缺失点云,具有较好的鲁棒性和抗干扰性。

To improve the registration accuracy of a point cloud,the problem of poor robustness of the iterative closest point(ICP)algorithm under the condition of noise interference and data loss caused by a single feature needs to be solved.Accordingly,a point cloud registration method based on weighting neighborhood surface deformation information is proposed.First,to simplify the neighborhood information of points,a neighborhood construction method based on the number of neighboring points as the constraint is proposed,and considering the influence of neighbors on the sampling points,a weighting method is introduced to improve the extraction efficiency of the intrinsic shape signature(ISS)feature point extraction algorithm.Second,the mean value of the normal vector inner product of the neighborhood is calculated to perform the second feature point extraction of the point cloud.Then,the fast point feature histogram(FPFH)is used to describe the feature,and the double constraint condition is used to determine the matching point pair relationship.Finally,in the registration phase,accurate registration is achieved by using the bidirectional k-tree ICP(DTICP)algorithm.Experiment results reveal that the proposed algorithm can effectively register missing point clouds in a noisy environment with better robustness and anti-interference compared with the classical ICP algorithm.