Precise measurement of position and attitude based on convolutional neural network and visual correspondence relationship
journal contributionposted 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.
- Computer Science
Published inIEEE Transactions on Neural Networks and Learning Systems
Pages2030 - 2041
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
- AM (Accepted Manuscript)
Rights holder© IEEE
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