  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.
