Estimation of Point Cloud Object Pose Using Particle Swarm Optimization

2018 ◽  
Author(s):  
Ge Yu ◽  
Ming Liu ◽  
Tianyu Liu ◽  
Lili Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Point Cloud ◽  
Particle Swarm ◽  
Swarm Optimization

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

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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