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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Precise Measurement of Position and Attitude Based on Convolutional Neural Network and Visual Correspondence Relationship
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Precise Measurement of Position and Attitude Based on Convolutional Neural Network and Visual Correspondence Relationship

机译：基于卷积神经网络和视觉函数关系的位置和姿态精确测量

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摘要

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-to-end 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.
机译：准确测量位置和态度信息尤为重要。传统的测量方法通常需要高精度测量设备进行分析，导致高成本和适用性有限。基于视觉的测量方案需要解决复杂的视觉关系。随着相关领域的神经网络的广泛发展，有可能将它们应用于对象位置和态度。在本文中，我们提出了一种基于卷积神经网络的物体姿势测量方案，我们已成功实现了端到端位置和姿态检测。此外，为了有效地扩展测量范围并减少训练样本的数量，我们证明了每个维度中对象的独立性和提议的课后培训计划。同时，我们生成生成图像编码器以保证在实际应用中的训练模型的检测性能。

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