基于超体素双向最近邻距离比的点云配准方法

Point cloud registration method based on supervoxel bidirectional nearest neighbor distance ratio

针对点云数据冗余和配准精度低的问题,提出了一种基于超体素的双向最近邻距离比匹配的配准方法。首先,利用超体素提取了具有稳定结构的目标特征点,同时提出了利用点云厚度分层进行非迭代的阈值去噪方法;然后,利用FPFH进行特征描述,提出了双向最近邻距离比方法对点云进行了初始配准;最后,提出了基于双级阈值的点云精确配准方法。采用标准数据库模型进行仿真分析,验证了算法的有效性。结果表明:本方法能有效剔除漂移噪声体素,配准精度高,鲁棒性强。与其他方法对比,在配准时间相近时,本文算法的配准精度提高74.2%;在噪声占比为6%和10%时,配准精度均提高67%以上。

Aiming at the problems of redundancy and low registration accuracy of the point cloud data,a bidirectional nearest neighbor distance ratio registration method based on supervoxel is proposed in this paper. Firstly,the target feature points with stable structure are extracted by the supervoxel,and a noniterative threshold denoising method based on point cloud thickness stratification is proposed;then,using FPFH for feature description,and a bidirectional nearest neighbor distance ratio method is proposed to register the point cloud;finally,an accurate point cloud registration method based on two-level threshold is proposed. The standard database model is used for simulation analysis to verify the effectiveness of the algorithm. The results show that the proposed method in this paper can effectively eliminate the drift noise voxels,and the algorithm has high registration accuracy and strong robustness. Compared with other methods,when the registration time is close,the registration accuracy of this algorithm is improved by 74.2%;When the noise ratio is 6% and 10%,the registration accuracy is improved by more than 67%.