Research on target feature extraction and location positioning with machine learning algorithm

The accurate positioning of target is an important link in robot technology. Based on machine learning algorithm, this study firstly analyzed the location positioning principle of binocular vision of robot, then extracted features of the target using speeded-up robust features (SURF) method, positio...

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Autor principal:	Li Licheng
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	intelligent robot binocular vision feature extraction machine learning speeded-up robust features back propagation neural network 68t40 Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/b7a60cd6a4c04aa295b39aa6f2145e67
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id	oai:doaj.org-article:b7a60cd6a4c04aa295b39aa6f2145e67
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spelling	oai:doaj.org-article:b7a60cd6a4c04aa295b39aa6f2145e672021-12-05T14:10:51ZResearch on target feature extraction and location positioning with machine learning algorithm2191-026X10.1515/jisys-2020-0072https://doaj.org/article/b7a60cd6a4c04aa295b39aa6f2145e672020-12-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0072https://doaj.org/toc/2191-026XThe accurate positioning of target is an important link in robot technology. Based on machine learning algorithm, this study firstly analyzed the location positioning principle of binocular vision of robot, then extracted features of the target using speeded-up robust features (SURF) method, positioned the location using Back Propagation Neural Networks (BPNN) method, and tested the method through experiments. The experimental results showed that the feature extraction of SURF method was fast, about 0.2 s, and was less affected by noise. It was found from the positioning results that the output position of the BPNN method was basically consistent with the actual position, and errors in X, Y and Z directions were very small, which could meet the positioning needs of the robot. The experimental results verify the effectiveness of machine learning method and provide some theoretical support for its further promotion and application in practice.Li LichengDe Gruyterarticleintelligent robotbinocular visionfeature extractionmachine learningspeeded-up robust featuresback propagation neural network68t40ScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 429-437 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	intelligent robot binocular vision feature extraction machine learning speeded-up robust features back propagation neural network 68t40 Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	intelligent robot binocular vision feature extraction machine learning speeded-up robust features back propagation neural network 68t40 Science Q Electronic computers. Computer science QA75.5-76.95 Li Licheng Research on target feature extraction and location positioning with machine learning algorithm
description	The accurate positioning of target is an important link in robot technology. Based on machine learning algorithm, this study firstly analyzed the location positioning principle of binocular vision of robot, then extracted features of the target using speeded-up robust features (SURF) method, positioned the location using Back Propagation Neural Networks (BPNN) method, and tested the method through experiments. The experimental results showed that the feature extraction of SURF method was fast, about 0.2 s, and was less affected by noise. It was found from the positioning results that the output position of the BPNN method was basically consistent with the actual position, and errors in X, Y and Z directions were very small, which could meet the positioning needs of the robot. The experimental results verify the effectiveness of machine learning method and provide some theoretical support for its further promotion and application in practice.
format	article
author	Li Licheng
author_facet	Li Licheng
author_sort	Li Licheng
title	Research on target feature extraction and location positioning with machine learning algorithm
title_short	Research on target feature extraction and location positioning with machine learning algorithm
title_full	Research on target feature extraction and location positioning with machine learning algorithm
title_fullStr	Research on target feature extraction and location positioning with machine learning algorithm
title_full_unstemmed	Research on target feature extraction and location positioning with machine learning algorithm
title_sort	research on target feature extraction and location positioning with machine learning algorithm
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/b7a60cd6a4c04aa295b39aa6f2145e67
work_keys_str_mv	AT lilicheng researchontargetfeatureextractionandlocationpositioningwithmachinelearningalgorithm
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Research on target feature extraction and location positioning with machine learning algorithm

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