• 图像处理 •

### 基于改进PSO-TrICP算法的点云配准

1.  (1.广西大学计算机与电子信息学院，广西南宁530004；2.广西多媒体通信与网络技术重点实验室，广西南宁530004)
• 出版日期:2022-06-08 发布日期:2022-06-08
• 作者简介:梁正友(1968—)，男，广西天等人，教授，博士，研究方向：计算机视觉，无线传感器网络，并行分布式计算，人工智能，E-mail： zhyliang@gxu.edu.cn；王璐(1996—)，女，福建福州人，硕士研究生，研究方向：计算机视觉，图像处理，机器学习，E-mail： 709243264@qq.com； 李轩昂(1993—)，男，广西玉林人，硕士研究生，研究方向：计算机视觉，图像处理，机器学习，E-mail： 931476138@qq.com； 杨锋(1979—)，男，广西玉林人，副教授，博士，研究方向：人工智能，网络信息安全，大数据与高性能计算，精准医学，E-mail：yf@gxu.edu.cn。
• 基金资助:
国家自然科学基金资助项目（61861004）

### A Point Cloud Registration Algorithm Combining Improved PSO Algorithm and TrICP Algorithm

1. (1. School of Computer and Electronic Information, Guangxi University, Nanning 530004, China;
2. Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning 530004, China)
• Online:2022-06-08 Published:2022-06-08

Abstract: Aiming at the problem that the traditional iterative closest point (ICP) algorithm is easy to fall into the problem of local optimality when the initial spatial position deviation is large, a point cloud registration method combining improved PSO-TrICP algorithm is proposed. Firstly, the traditional particle swarm optimization (PSO) algorithm is improved by introducing similarity measurement criterion of fitness to adjust the updating mode of particles. Then, the mean value of the historical global optimal solution of each iteration is added as a new learning factor to avoid the phenomenon of “precocity”; Secondly, the rigid transformation parameters and the overlap rate between the point clouds are used to form the particles, and the improved PSO algorithm is used to provide a good initial relative position; Finally, the space transformation between point clouds is estimated with trimmed iterative closest point (TrICP) algorithm. Experimental results show that the improved PSO-TRICP algorithm has better registration accuracy and operation efficiency than the similar registration algorithms proposed in recent years, and has better robustness.