Marker-based optical tracking systems are widely used in augmented reality, medical navigation and industrial applications. We propose a model for the prediction of the target registration error (TRE) in these kinds of tracking systems by estimating the fiducial location error (FLE) from two-dimensional errors on the image plane and propagating that error to a given point of interest. We have designed a set of experiments in order to estimate the actual parameters of the model for any given tracking system. We present the results of a study which we used to demonstrate the effect of different sources of error. The method is applied to real applications to show the usefulness for any kind of augmented reality system. We also present a set of tools that can be used to visualize the accuracy at design time.

Augmented reality (AR) provides a natural interface to the "calm" pervasive technology anticipated in large-scale ubiquitous computing environments. However, the range of classic AR applications has been limited by the scope, range and cost of sensors used for tracking. Hybrid tracking approaches can go some way to extending this range. We propose an approach, called ubiquitous tracking, in which data from widespread and diverse heterogeneous tracking sensors is automatically and dynamically fused, and then transparently provided to applications. A formal model represents spatial relationships between objects as a graph attributed with quality-of-service parameters. This paper presents a software implementation, in which a dynamic data flow network of distributed software components is thereby constructed in response to queries and optimisation criteria specified by applications. This implementation is demonstrated using a small laboratory example, and larger setups modelled in a simulation environment.

In the past, architectures of augmented reality systems have been widely different and tailored to specific tasks. In this paper, we use the example of the SHEEP game to show how the structural flexibility of DWARF, our component-based distributed wearable augmented reality framework, facilitates a rapid prototyping and online development process for building, debugging and altering a complex, distributed, highly interactive AR system. The SHEEP system was designed to test and demonstrate the potential of tangible user interfaces which dynamically visualize, manipulate and control complex operations of many inter-dependent processes. SHEEP allows the users more freedom of action and forms of interaction and collaboration, following the tool metaphor that bundles software with hardware in units that are easily understandable to the user. We describe how we developed SHEEP, showing the combined evolution of framework and application, as well as the progress from rapid prototype to final demonstration system. The dynamic aspects of DWARF facilitated testing and allowed us to rapidly evaluate new technologies. SHEEP has been shown successfully at various occasions. We describe our experiences with these demos.

Mobile augmented reality applications promise substantial savings in time and costs for product designers, in particular, for large products requiring scale models and expensive clay mockups (e.g., cars). Such applications are novel and introduce interesting challenges when attempting to describe them to potential users and stakeholders. For example, it is difficult, a priori, to assess the nonfunctional requirements of such applications and anticipate the usability issues that the product designers are likely to raise. In this paper, we describe our efforts to develop a proof-of-concept AR system for car designers. Over the course of a year, we developed two prototypes, one within a university context, the other at a car manufacturer. The lessons learned from both efforts illustrate the technical and human challenges encountered when closely collaborating with the end user in the design of a novel application.