P. Keitler Management of Tracking and Tracking Accuracy in Industrial Augmented Reality Environments Dissertation (PhD thesis), Fakultät für Informatik, Technische Universität München, April 2011 (online version) (bib)
	Industrial Augmented Reality (IAR) techniques can help to increase the productivity of certain work processes by augmenting the physical scene with virtual information. IAR brings together tasks that are traditionally performed off-line on purely virtual data, such as industrial design, geometric layout, or metrologic evaluations, with on-line tasks in the domain of physical objects such as prototypes, mockups, facilities or repetition parts. This allows for a broad range of new, integrated applications. Real-time position and orientation tracking of physical objects is needed to register these objects with the virtual world. It can be implemented based on a variety of sensors providing spatial measurements on an optical, inertial, or acoustic basis. However, tracking for IAR is often difficult to implement, due to the constraints imposed by the working environment such as electro magnetic interference, dirt/dust, noise, bad illumination conditions, vibrations, occlusion, and interference with existing work processes. Sometimes, the flexibility to quickly setup and dismantle the system is required. Nevertheless, the tracking has to be robust and meet the accuracy requirements imposed by the intended application. Often, this can only be accomplished by a heterogeneous multi-sensor tracking environment, a fact that complicates the task of registering the various coordinate frames of tracking systems, sensors, markers, and display devices with respect to one another. This thesis describes a generic approach to deal with the complexity of heterogeneous tracking environments. It supports the IAR engineer throughout the various design and implementation phases of an industrial IAR scenario. An abstract semantic modeling concept based on spatial relationship graphs (SRG) and its implementation in a graphical data flow editor is presented. Modeling is based on reusable design patterns which represent atomic sensor drivers and geometric algorithms as well as complex state-of-the- art solutions. Real-time data flow networks can be generated automatically from the SRG and are guaranteed to be semantically correct. Still, the data flow layer remains directly accessible through a round-trip engineering approach. Based on the SRG concept, simulation and analysis tools for a rigorous management of tracking accuracy are described. Monte Carlo simulation helps IAR engineers to understand the proposed system and to identify critical design issues, even before actual hardware deployment. Expert analysis tools help them in system validation and maintenance where simulation results have to be confirmed based on real measurement data. End-user analysis tools facilitate recurring quality checks by on-site personnel during regular system operation. The system has been implemented and used successfully in two real industrial settings. The described approach simplifies and standardizes the setup, operation, and maintenance of such IAR tracking environments. . Techniken der Erweiterten Realität können die Produktivität von industriellen Arbeitsprozessen erhöhen, indem die Sicht des Nutzers mit virtuellen Informationen angereichert wird. Dazu muss Position und Orientierung realer Objekte in Echtzeit bestimmt werden, um sie mit der virtuellen Welt registrieren zu können. Für Dieses Objekt-Tracking werden verschiedene Messprinzipien, insbesondere optische, inertiale oder akkustische, verwendet. Oft ist eine Kombination verschiedener Verfahren notwendig, um die angestrebte Robustheit und Genauigkeit zu erreichen. Diese Arbeit beschreibt einen generischen Ansatz zum Umgang mit heterogenen Tracking-Umgebungen, basierend auf räumlichen Abhängigkeitsgraphen. Das graphische Modellierkonzept vereinfacht den Umgang mit statischen und dynamischen Abhängigkeiten erheblich. Darauf aufbauend werden Simulations- und Analysewerkzeuge beschrieben, die einen umfassenden Umgang mit Tracking-Genauigkeit erlauben.
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