智能系统学报2025，Vol.20Issue(3)：621-630,10.DOI:10.11992/tis.202403022
基于特征融合和网络采样的点云配准
Point cloud registration based on feature fusion and network sampling
摘要
Abstract
To solve the issue of easily losing key points during lower sampling,which affects the registration accuracy during point cloud registration,a registration method is proposed based on network sampling and feature fusion,and this method improves registration accuracy and speed.Based on the PointNet classification network,we design a deep learn-ing(DL)network-based method for key point extraction.The method fuses local features with global features to obtain the feature matrix with fixed characteristics and uses DL to automatically optimize the parameters when calculating the corresponding matrix.Finally,we use weighted singular value decomposition to obtain the transformation matrix and complete the registration.Our experiments using the ModelNet40 dataset reveal that the time consumed for the process is reduced by 45.36％compared with that consumed by farthest point sampling.Compared with the RPM-Net algorithm,the mean square errors of the translation and rotation matrices obtained by the proposed method are reduced by 5.67％and 13.1％,respectively.Further,the designed model was subjected to experiments,which proved the effectiveness of the algorithm in registering real objects.关键词
点云配准/特征融合/深度学习/网络采样/三维视觉/局部特征/全局特征/特征提取Key words
point cloud registration/feature fusion/deep learning/network sampling/3D vision/local feature/global feature/feature extraction分类
信息技术与安全科学引用本文复制引用
陆军,王文豪,杜宏劲..基于特征融合和网络采样的点云配准[J].智能系统学报,2025,20(3):621-630,10.基金项目
黑龙江省自然科学基金项目(F201123). （F201123）
