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Modern simulators are inefficient in terms of cost as well as space. Furthermore it is not reaching its full potential since plenty of characteristics of the real world, like proper Field–of–View, are implemented baldy, or not at all. The trending technology Virtual Reality creates a model of the real world, which is interactable by new and more intuitive control–schemes. Thats how the virtual reality creates a much stronger immersion which then increases the effektivity of any simulation. On the one hand the technology should enable creating cheaper alternatives to the more traditional hardware by minimizing the real hardware in the simulation. On the other hand virtual reality can be used in existing simulators, usually as output device, to increase the effectivity of said simulators. Currently there are two state–of–the–art devices on the market, the HTC Vive and the Oculus Rift. Both are so called Head Mounted Displays, which help creating the necessary immersion for the technology. Both of these devices are analyzed based on hardwarespezification, APIs and control schemes. During this bachelor thesis a protoype is created to properly present the features of this technology. This prototype solely relies on the virtual part of the software, except the HTC Vive which is utilized as output and input device. This example should clarify that with minimal costs and effort one can still create a quite efficient simulation.

Especially in the field of augmented reality, spatial orientation is of high relevance for applications. Apart from the position in the three-dimensional space, this is one of the most important factors to be able to represent virtual objects in space precisely and correctly. The orientation in the case of so-called head-mounted displays, such as the Microsoft HoloLens, is equal to the direction of view, whereby this data decides about which information is displayed in the user´s field of view and which is not. From the current state of the art this is primarily achieved marker-based with visual indications in the environment and/or with the help of sensors. Based on the augmented reality frameworks Vuforia and Kudan AR, this paper discusses two common possibilities for marker-based determination of the own orientation and describes the problem of this feature-based approach that applications depend on a single fixed position or on an enormous number of reference images for each virtual object. This lack of practicality leads to the necessity of an alternative approach from which the aim of this discourse is derived to find a feature-independent, image-based attempt, that determines the orientation based on the calculation of the image shift. This approach is based on the hypothesis, which states that the rotation around one’s own axis can be determined in a simplified manner by a translation of the input images. The implementation of this hypothesis is evaluated premised on several test scenarios. Based on these results, the question of this paper “Is it possible to determine outdoor the precise orientation in space at a constant starting point with image-based processes?” is answered and is considered as underpinned because of the evaluation result of a median difference of 0.00015° between the real and the calculated rotation.

There are different frameworks for the area of the currently omnipresent technology Augmented Reality which support developers creating an application in this domain with various for this area suitable functionalities. This paper deals with the definition and the fundamentals of Augmented Reality. Afterwards there is given a differentiation to the related technology Virtual Reality, to come over to an explanation of the technical basis in terms of software as well as hardware, to well-known problems associated with the use of Augmented Reality systems. Based on this knowledge there follows the definition of Augmented Reality Frameworks, the various basic components and a small selection of design patterns which are implemented by several software modules. Furthermore, an overview of existing solutions from various manufacturers will be discussed after passing on typical functionalities which are provided by several of these frameworks. Finally, based on predefined premises the frameworks Vuforia and Kudan AR Engine are selected, will be explained with the aid of prototypes and are compared in a final chapter in the disciplines of marker-based and marker-less tracking. With the actual question of this elaborate (Which AR-frameworks exist and which functionality do those covers?) as ubiquitous problem statement, the attention should be drawn to differences which may be important in the implementation of applications in the field of Augmented Reality to create a technical fundamental as well as a basis for conceptual understanding.

In the last couple of years, the terms Virtual Reality and Augmented Reality increased in importance. For most of the provided functionality a basic VR/AR-device is enough. On the other hand, there are some areas of application where additional technology is mandatory. An example, where additional technology is needed, is the training of emergency drivers. For this scenario and exact position of the windscreen is needed. So that, additional hazards can be displayed. This thesis is focusing on this problem and tries to show the possibilities with edge detection algorithms to detect windscreens in vehicles. Therefore, there are two central questions. “It is possible to detect windows purely by means of edge detection” and “Which are the most common algorithms of edge detection and what are their main advantages and disadvantages”. To answer these two questions, this thesis is split into two parts. On the one hand, there is a theoretical part, and on the other hand, there are elaborations of prototypes. The theoretical part, describes the basic functionality of different edge detection algorithms. As well as it checks the possibility to use it for detecting windows. Essentially, the following operators / procedures were considered: Roberts, Prewitt, Sobel, Kompass-Gradient, Kirsch, Marr-Hildreth und Canny. Three prototype were developed, to compare the algorithms in its functionality. The selected ones are Marr-Hildreth and Canny, because they can be parameterized and therefore they are quite well for this scenario. The third one is Prewitt, which is used to compare it to the non-parameterized ones. With the results of the prototypes, it is now able to check which of them solves which scenario best. It also describes, which settings and parameters are needed to achieve this result. For the prototypes following algorithms were used: Prewitt, Marr-Hildreth und Canny. The achieved results of the prototypes were very different. In some scenarios, the edge detection algorithms had trouble to deal with the scenario. Whereas in some scenarios the results were good. Generally, the prototypes showed the typical problems with edge detection algorithms. Disturbing factors such as rushing or blurring were a common problem. The use of edge detection algorithms to detect windows is possible for some standard scenarios. Especially when there are very few interfering factors. To enable the detection in other scenarios, additional procedures are required. An example therefore would be object detection.