The choice of poses for camera calibration with planar patterns is only rarely considered - yet the calibration precision heavily depends on it. This work presents a pose selection method that finds a compact and robust set of calibration poses and is suitable for interactive calibration. Consequently, singular poses that would lead to an unreliable solution are avoided explicitly, while poses reducing the uncertainty of the calibration are favoured. For this, we use uncertainty propagation. Our method takes advantage of a self-identifying calibration pattern to track the camera pose in real-time. This allows to iteratively guide the user to the target poses, until the desired quality level is reached. Therefore, only a sparse set of key-frames is needed for calibration. The method is evaluated on separate training and testing sets, as well as on synthetic data. Our approach performs better than comparable solutions while requiring 30% less calibration frames.

Although AR has a long history in the area of maintenance and service-support in industry, there still is a lack of lightweight, yet practical solutions for handheld AR systems in everyday workflows. Attempts to support complex maintenance tasks with AR still miss reliable tracking techniques, simple ways to be integrated into existing maintenance environments, and practical authoring solutions, which minimize costs for specialized content generation.

Although AR has a long history in the area of maintenance and service-support in industry, there still is a lack of lightweight, yet practical solutions for handheld AR systems in everyday workflows. Attempts to support complex maintenance tasks with AR still miss reliable tracking techniques, simple ways to be integrated into existing maintenance environments, and practical authoring solutions, which minimize costs for specialized content generation. We present a general, customisable application framework, allowing to employ AR and VR techniques in order to support technicians in their daily tasks. In contrast to other systems, we do not aim to replace existing support systems such as traditional manuals. Instead we integrate well-known AR- and novel presentation techniques with existing instruction media. To this end practical authoring solutions are crucial and hence we present an application development system based on web-standards such as HTML,CSS and X3D.

Using Head-up-Displays (HUD) for Augmented Reality requires to have an accurate internal model of the image generation process, so that 3D content can be visualized perspectively correct from the viewpoint of the user. We present a generic and cost-effective camera-based calibration for an automotive HUD which uses the windshield as a combiner. Our proposed calibration model encompasses the view-independent spatial geometry, i.e. the exact location, orientation and scaling of the virtual plane, and a view-dependent image warping transformation for correcting the distortions caused by the optics and the irregularly curved windshield. View-dependency is achieved by extending the classical polynomial distortion model for cameras and projectors to a generic five-variate mapping with the head position of the viewer as additional input. The calibration involves the capturing of an image sequence from varying viewpoints, while displaying a known target pattern on the HUD. The accurate registration of the camera path is retrieved with state-of-the-art vision-based tracking. As all necessary data is acquired directly from the images, no external tracking equipment needs to be installed. After calibration, the HUD can be used together with a head-tracker to form a head-coupled display which ensures a perspectively correct rendering of any 3D object in vehicle coordinates from a large range of possible viewpoints. We evaluate the accuracy of our model quantitatively and qualitatively.