Augmented Reality has been subject to various integration efforts within industries due to its ability to enhance human machine interaction and understanding. Neural networks have achieved remarkable results in areas of computer vision, which bear great potential to assist and facilitate an enhanced Augmented Reality experience. However, most neural networks are computationally intensive and demand huge processing power thus, are not suitable for deployment on Augmented Reality devices. In this work we propose a method to deploy state of the art neural networks for real time 3D object localization on augmented reality devices. As a result, we provide a more automated method of calibrating the AR devices with mobile robotic systems. To accelerate the calibration process and enhance user experience, we focus on fast 2D detection approaches which are extracting the 3D pose of the object fast and accurately by using only 2D input. The results are implemented into an Augmented Reality application for intuitive robot control and sensor data visualization. For the 6D annotation of 2D images, we developed an annotation tool, which is, to our knowledge, the first open source tool to be available. We achieve feasible results which are generally applicable to any AR device thus making this work promising for further research in combining high demanding neural networks with Internet of Things devices.

中文翻译：

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使用 RGB 数据的移动机器人基于无标记深度学习的 6 自由度姿态估计

增强现实由于能够增强人机交互和理解能力，因此在行业内进行了各种集成工作。神经网络在计算机视觉领域取得了显著成果，在协助和促进增强现实体验方面具有巨大潜力。然而，大多数神经网络是计算密集型的，需要巨大的处理能力，因此不适合部署在增强现实设备上。在这项工作中，我们提出了一种在增强现实设备上部署最先进的神经网络以进行实时 3D 对象定位的方法。因此，我们提供了一种更自动化的方法来校准带有移动机器人系统的 AR 设备。为了加快校准过程并增强用户体验，我们专注于快速 2D 检测方法，该方法仅使用 2D 输入快速准确地提取对象的 3D 姿态。结果被实施到增强现实应用程序中，用于直观的机器人控制和传感器数据可视化。对于 2D 图像的 6D 注释，我们开发了注释工具，据我们所知，这是第一个可用的开源工具。我们取得了普遍适用于任何 AR 设备的可行结果，从而使这项工作有望进一步研究将高要求的神经网络与物联网设备相结合。我们开发了一个注释工具，据我们所知，这是第一个可用的开源工具。我们取得了普遍适用于任何 AR 设备的可行结果，从而使这项工作有望进一步研究将高要求的神经网络与物联网设备相结合。我们开发了一个注释工具，据我们所知，这是第一个可用的开源工具。我们取得了普遍适用于任何 AR 设备的可行结果，从而使这项工作有望进一步研究将高要求的神经网络与物联网设备相结合。