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Zusammenfassung: Die Montanuniversität Leoben leitet das europaweite Projekt MiReBooks des EIT RawMaterials, das die Erstellung neuer Bergbaulehrbücher zum Ziel hat. Mixed Reality Books, kurz MiReBooks, steht für interaktive Lehrbücher, die durch die Integration von digitalen Visualisierungselementen, wie z. B. Virtual und Augmented Reality, die Ausbildung im Bereich Bergbau modernisieren sollen. Ziel ist es, zukünftig in ganz Europa Lehr-und Lernmaterialien für das Bergbaustudium zur Verfügung zu haben, die als neue internationale Standards die traditionelle Wissensvermittlung um erfahr-und erlebbare Komponenten erweitern. Der Ansatz dient auch als Modell für weitere Fachrichtungen.

The learning systems based on the solution of real projects have proved to be efficient in the different educational levels. With the use of simulation environments these systems achieve a combination between the student monitoring and discovery learning. In this work we present tools that enable the creation of a plan in a collaborative way and the use of simulation in learning communities to the solution of design problems applied to the domotics domain

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In this paper, we address the challenge of estimating the 6DoF pose of objects in 2D equirectangular images. This solution allows the transition to the objects’ 3D model from their current pose. In particular, it finds application in the educational use of 360° videos, where it enhances the learning experience of students by making it more engaging and immersive due to the possible interaction with 3D virtual models. We developed a general approach usable for any object and shape. The only requirement is to have an accurate CAD model, even without textures of the item, whose pose must be estimated. The developed pipeline has two main steps: vehicle segmentation from the image background and estimation of the vehicle pose. To accomplish the first task, we used deep learning methods, while for the second, we developed a 360° camera simulator in Unity to generate synthetic equirectangular images used for comparison. We conducted our tests using a miniature truck model whose CAD was at our disposal. The developed algorithm was tested using a metrological analysis applied to real data. The results showed a mean difference of 1.5 with a standard deviation of 1 from the ground truth data for rotations, and 1.4 cm with a standard deviation of 1.5 cm for translations over a research range of ±20 and ±20 cm, respectively.