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Download filePrecise measurement of position and attitude based on convolutional neural network and visual correspondence relationship
journal contribution
posted on 2019-09-27, 12:53 authored by Jiachen Yang, Jiabao Man, Meng Xi, Xinbo Gao, Wen Lu, Qinggang MengQinggang MengAccurate 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 SystemsVolume
31Issue
6Pages
2030 - 2041Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher 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
2019-08-26Copyright date
2019ISSN
2162-237XeISSN
2162-2388Publisher version
Language
- en