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.

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 3-D Point Cloud Registration Algorithm Based on Greedy Projection Triangulation

2018 ◽  
Vol 8 (10) ◽  
pp. 1776 ◽  
Author(s):  
Jian Liu ◽  
Di Bai ◽  
Li Chen
Keyword(s):  
Machine Vision ◽  
Point Cloud ◽  
Three Dimensional ◽  
Target Object ◽  
Initial Alignment ◽  
Registration Accuracy ◽  
Point Cloud Registration ◽  
Registration Algorithm ◽  
Feature Information ◽  
Complex Target

To address the registration problem in current machine vision, a new three-dimensional (3-D) point cloud registration algorithm that combines fast point feature histograms (FPFH) and greedy projection triangulation is proposed. First, the feature information is comprehensively described using FPFH feature description and the local correlation of the feature information is established using greedy projection triangulation. Thereafter, the sample consensus initial alignment method is applied for initial transformation to implement initial registration. By adjusting the initial attitude between the two cloud points, the improved initial registration values can be obtained. Finally, the iterative closest point method is used to obtain a precise conversion relationship; thus, accurate registration is completed. Specific registration experiments on simple target objects and complex target objects have been performed. The registration speed increased by 1.1% and the registration accuracy increased by 27.3% to 50% in the experiment on target object. The experimental results show that the accuracy and speed of registration have been improved and the efficient registration of the target object has successfully been performed using the greedy projection triangulation, which significantly improves the efficiency of matching feature points in machine vision.

scholarly journals Particle Swarm Optimization-based Support Vector Machine Method for Sentiment Analysis in OVO Digital Payment Applications

2021 ◽  
Vol 4 (2) ◽  
pp. 232-239
Author(s):  
Retno Sari ◽  
Ratih Yulia Hayuningtyas
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Particle Swarm ◽  
Support Vector ◽  
In Ovo ◽  
Machine Method ◽  
Swarm Optimization ◽  
Support Vector Machine Method ◽  
The Right

Sentiment analysis is used to analyze reviews of a place or item from an application or website that then classified the review into positive reviews or negative reviews. reviews from users are considered very important because it contains information that can make it easier for new users who want to choose the right digital payment. Reviews about digital payment ovo are so much that it is difficult for prospective users of ovo digital payment applications to draw conclusions about ovo digital payment information. For this reason, a classification method is needed in this study using support vector machine and PSO methods. In this study, we used 400 data that were reduced to 200 positive reviews and 200 negative reviews. The accuracy obtained by using the support vector machine method of 76.50% is in the fair classification, while the accuracy obtained by using the support vector machine and Particle Swarm Optimization (PSO) method is 82.75% which is in good classification.

scholarly journals Prediction of Successful Elearning Based on Activity Logs with Selection of Support Vector Machine based on Particle Swarm Optimization

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Elin Panca Saputra ◽  
Sukmawati Angreani Putri ◽  
Indriyanti Indriyanti
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Attribute Selection ◽  
Support Vector ◽  
Swarm Optimization ◽  
Student Activities ◽  
Svm Algorithm ◽  
The Right ◽  
Selection Of

Prediction is a systematic estimate that identifies past and future information, we predict the success of learning with elearning based on a log of student activities. In our current study we use the Support vector machine (SVM) method which is comparable with Particle Swarm Optimization. It is known that SVM has a very good generalization that can solve a problem. however, some of the attributes in the data can reduce accuracy and add complexity to the Support Vector Machine (SVM) algorithm. It is necessary for existing tribute selection, therefore using the Particle swarm optimization (PSO) method is applied to the right attribute selection in determining the success of elearning learning based on student activity logs, because with the Swarm Optimization (PSO) method can increase accuracy in determining selection of attributes.

scholarly journals Formulation and Anticipated Approach for Developing a New Business through Integrated Strategic Morphological Analysis and Integrated Fuzzy Approach and Estimate the Cost of the Integration of PSO and BP Neural Network in the Plastic Injection Molding Industry

2017 ◽  
Vol 9 (1) ◽  
pp. 239
Author(s):  
Seyed Ashkan Hoseini Shekarabi ◽  
Behrouz Dorri
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Environmental Factors ◽  
Business Model ◽  
Particle Swarm ◽  
Fuzzy Decision Making ◽  
Medium Term ◽  
Software Environment ◽  
Swarm Optimization ◽  
The Right

In this article we have tried to identify the factors by which industry experts predict medium-term future, then the relationship between the environmental factors determined by comparing the morphology characterized couple, this relationship is obtained through interviews with experts economy. The experts in economic conditions and environmental factors determine the medium-term future. Finally, according to industry experts on space environmental conditions, inter-organizational scenarios to determine the ranking. Using fuzzy decision-making through the evaluation and ranking of organizational standpoint, the most appropriate one is selected that has the features, dimensions and unique circumstances applicable to the environment. Due to the globalization of business in recent years, managers and business owners are looking to cut costs and accurate and realistic estimates of cost, due to its ability to make the right decision about the products and the future of their business. At the end of a cost estimate for superior business model that obtained by ranking methods propagation is back propaganda neural network Particle swarm optimization. It is also complex and covers defects traditional methods. Hybrid algorithm can not only take advantage of the ability to search for a strong global particle swarm optimization, but also be robust search capability regional propagation neural network as well. The corresponding operation in MATLAB software environment (MATLAB) is implemented. Finally, model related to the choice of business, business model and cost estimates provided kitchenware and printing and packaging businesses are adaptable to future requirements and trends toward this part of the industry for the benefit of the organization.

scholarly journals Using Particle Swarm Optimization as Pathfinding Strategy in a Space with Obstacles

2021 ◽  
Author(s):  
David
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Population Based ◽  
Target Point ◽  
Adaptive Optimization ◽  
Swarm Optimization ◽  
Algorithm Performance ◽  
Long Time

Particle swarm optimization (PSO) is a search algorithm based on stochastic and population-based adaptive optimization. In this paper, a pathfinding strategy is proposed to improve the efficiency of path planning for a broad range of applications. This study aims to investigate the effect of PSO parameters (numbers of particle, weight constant, particle constant, and global constant) on algorithm performance to give solution paths. Increasing the PSO parameters makes the swarm move faster to the target point but takes a long time to converge because of too many random movements, and vice versa. From a variety of simulations with different parameters, the PSO algorithm is proven to be able to provide a solution path in a space with obstacles.

Particle Swarm Inspired Timetabling for ICT Courses

2012 ◽  
Vol 263-266 ◽  
pp. 2138-2145
Author(s):  
Oi Mean Foong ◽  
Syamilla Bt Rahim
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Complex Problem ◽  
Hard Problem ◽  
Np Hard ◽  
Swarm Optimization ◽  
University Course Timetabling ◽  
Hard Constraints ◽  
Np Hard Problem ◽  
The Right

University course timetabling is a complex problem which must satisfy a list of constraints in order to allocate the right timeslots and venues for various courses. The challenge is to make the NP-hard problem user-friendly, highly interactive and faster run time complexity of algorithm. The objective of the paper is to propose Particle Swarm Optimization (PSO) timetabling model for Undergraduate Information and Communication Technology (ICT) courses. The PSO model satisfies hard constraints with minimal violation of soft constraints. Empirical results show that the rds: NP hard problem, timetabling, particle swarm optimization

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