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Precise measurement of position and attitude based on convolutional neural network and visual correspondence relationship

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posted on 2019-09-27, 12:53 authored by Jiachen Yang, Jiabao Man, Meng Xi, Xinbo Gao, Wen Lu, Qinggang MengQinggang Meng
Accurate measurement of position and attitude information is particularly important. Traditional measurement methods generally require high-precision measurement equipment for analysis, leading to high costs and limited applicability. Vision-based measurement schemes need to solve complex visual relationships. With the extensive development of neural networks in related fields, it has become possible to apply them to the object position and attitude. In this paper, we propose an object pose measurement scheme based on convolutional neural network and we have successfully implemented end-toend position and attitude detection. Furthermore, to effectively expand the measurement range and reduce the number of training samples, we demonstrated the independence of objects in each dimension and proposed subadded training programs. At the same time, we generated generating image encoder to guarantee the detection performance of the training model in practical applications.

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Neural Networks and Learning Systems

Volume

31

Issue

6

Pages

2030 - 2041

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2019-08-26

Copyright date

2019

ISSN

2162-237X

eISSN

2162-2388

Language

  • en

Depositor

Prof Qinggang Meng

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