计算机技术与发展2025，Vol.35Issue(5)：97-105,9.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0002
基于双重注意力和匹配矩阵优化的点云配准算法
Point Cloud Registration Algorithm Based on Dual Attention and Matching Matrix Optimization
摘要
Abstract
To address the issues of low registration accuracy and poor robustness in point cloud registration algorithms caused by noise,mismatches,and missing correspondences,a point cloud registration algorithm integrating dual attention and matching matrix optimization is proposed.Firstly,to improve the registration results caused by noise points,a dual attention module combining channel attention and spatial attention is designed,where noisy points are assigned lower weights,this allows the model to focus more on important or relevant information.Secondly,a matching matrix optimization module is designed by incorporating both local and global information of feature points,enabling the model to fully utilize the multi-level features of the point cloud data,thereby improving the registration accuracy.Finally,validation is conducted on the synthetic dataset ModelNet40,the real-world indoor dataset 7 Scenes,and the real-world outdoor dataset KITTI.In the point cloud registration experiments on ModelNet40 with high noise,7Scenes,and KITTI,the root mean square errors of the rotation matrix and translation vector were reduced to 0.665 7 and 0.001 7,0.079 6 and 0.000 9,2.061 7 and 0.041 7,re-spectively.The experimental results demonstrate that the proposed method effectively reduces missed matches and eliminates mismatches while minimizing the influence of noise on the model,thereby improving the accuracy and robustness of point cloud registration.关键词
点云配准/通道注意力/空间注意力/匹配矩阵优化/深度学习Key words
point cloud registration/channel attention/spatial attention/matching matrix optimization/deep learning分类
信息技术与安全科学引用本文复制引用
姬硕,胡立华,张素兰,胡建华,王欣波..基于双重注意力和匹配矩阵优化的点云配准算法[J].计算机技术与发展,2025,35(5):97-105,9.基金项目
国家自然科学基金资助项目(62273248) （62273248）
山西省自然科学基金资助项目(202103021224285) （202103021224285）
中科院科技服务网络计划(STS-HP-202202) （STS-HP-202202）
