scholarly journals A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method

2014 ◽  
Vol 2 (1) ◽  
pp. 32-38 ◽  
Author(s):  
Quan Yu ◽  
Kesheng Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Point Cloud ◽  
Particle Swarm ◽  
Point Method ◽  
Iterative Closest Point ◽  
Alignment Method ◽  
Swarm Optimization ◽  
Closest Point Method

Point cloud registration based on improved iterative closest point method

2012 ◽  
Vol 20 (9) ◽  
pp. 2068-2076 ◽  
Author(s):  
王欣 WANG Xin ◽  
张明明 ZHANG Ming-ming ◽  
于晓 YU Xiao ◽  
章明朝 ZHANG Ming-chao
Keyword(s):  
Point Cloud ◽  
Point Method ◽  
Iterative Closest Point ◽  
Point Cloud Registration ◽  
Closest Point Method

scholarly journals A three-dimensional point cloud registration based on entropy and particle swarm optimization

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881433 ◽  
Author(s):  
Xu Zhan ◽  
Yong Cai ◽  
Ping He
Keyword(s):  
Particle Swarm Optimization ◽  
Point Cloud ◽  
Fitness Function ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Point Clouds ◽  
Particle Swarm Algorithm ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Swarm Optimization

A three-dimensional (3D) point cloud registration based on entropy and particle swarm algorithm (EPSA) is proposed in the paper. The algorithm can effectively suppress noise and improve registration accuracy. Firstly, in order to find the k-nearest neighbor of point, the relationship of points is established by k-d tree. The noise is suppressed by the mean of neighbor points. Secondly, the gravity center of two point clouds is calculated to find the translation matrix T. Thirdly, the rotation matrix R is gotten through particle swarm optimization (PSO). While performing the PSO, the entropy information is selected as the fitness function. Lastly, the experiment results are presented. They demonstrate that the algorithm is valuable and robust. It can effectively improve the accuracy of rigid registration.

scholarly journals A point cloud registration algorithm based on normal vector and particle swarm optimization

2019 ◽  
Vol 53 (3-4) ◽  
pp. 265-275 ◽  
Author(s):  
Xu Zhan ◽  
Yong Cai ◽  
Heng Li ◽  
Yangmin Li ◽  
Ping He
Keyword(s):  
Particle Swarm Optimization ◽  
Point Cloud ◽  
Particle Swarm ◽  
Target Point ◽  
Normal Vector ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Swarm Optimization ◽  
Registration Algorithm ◽  
The Right

Based on normal vector and particle swarm optimization (NVP), a point cloud registration algorithm is proposed by searching the corresponding points. It provides a new method for point cloud registration using feature point registration. First, in order to find the nearest eight neighbor nodes, the k-d tree is employed to build the relationship between points. Then, the normal vector and the distance between the point and the center gravity of eight neighbor points can be calculated. Second, the particle swarm optimization is used to search the corresponding points. There are two conditions to terminate the search in particle swarm optimization: one is that the normal vector of node in the original point cloud is the most similar to that in the target point cloud, and the other is that the distance between the point and the center gravity of eight neighbor points of node is the most similar to that in the target point cloud. Third, after obtaining the corresponding points, they are tested by random sample consensus in order to obtain the right corresponding points. Fourth, the right corresponding points are registered by the quaternion method. The experiments demonstrate that this algorithm is effective. Even in the case of point cloud data lost, it also has high registration accuracy.

Region segmentation of point cloud data based on improved particle swarm optimization fuzzy clustering

2017 ◽  
Vol 25 (4) ◽  
pp. 1095-1105 ◽  
Author(s):  
王晓辉 WANG Xiao-hui ◽  
吴禄慎 WU Lu-shen ◽  
陈华伟 CHEN Hua-wei ◽  
史皓良 SHI Hao-liang
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Point Cloud ◽  
Particle Swarm ◽  
Point Cloud Data ◽  
Swarm Optimization ◽  
Region Segmentation ◽  
Improved Particle Swarm Optimization ◽  
Cloud Data
Sign in / Sign up

Export Citation Format

Share Document