Virtual reality technology, with its continuous development, is gradually applied to healthcare, education, business, and other fields. In the application of the technology, position and attitude estimation, as a space positioning technology, is indispensable. Traditional pose estimation has the problems of high dependence on environment and great complexity. But convolutional neural network (CNN) and other technologies with computational intelligence provide a strong guarantee for the progress of pose estimation. This article, based on the theory of CNN in deep learning, as well as monocular vision system and target sample set with markers, proposes a method for estimation of target position and attitude, and at the same time, describes in detail a general way of making dataset with markers based on simulation environment. In this article, the comparative experiments of different network structures show that this measurement method can avoid manual extraction of complex image features, and realize fast, arbitrary and accurate measurement, which plays a key role in pose and attitude measurement. Moreover, the visual correspondence between the world coordinate system and the pixel coordinate system is proved effectively by quaternion.

中文翻译：

---

虚拟现实中基于深度学习的六维姿态估计

虚拟现实技术随着它的不断发展，逐渐应用于医疗、教育、商业等领域。在该技术的应用中，位置姿态估计作为一种空间定位技术是不可或缺的。传统的位姿估计存在环境依赖度高、复杂度大的问题。但卷积神经网络（CNN）等具有计算智能的技术为姿态估计的进展提供了强有力的保障。本文基于CNN在深度学习中的理论，以及单目视觉系统和带标记的目标样本集，提出了一种目标位置姿态估计方法，同时详细描述了一种通用的估计方法。基于模拟环境制作带有标记的数据集。在本文中，不同网络结构的对比实验表明，该测量方法可以避免人工提取复杂的图像特征，实现快速、任意、准确的测量，在位姿和姿态测量中起到关键作用。此外，四元数有效地证明了世界坐标系与像素坐标系之间的视觉对应关系。