A software framework for the development of projection-based augmented reality systems

Despite the large amount of methods and applications of augmented reality, there is little homogenization on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more concerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we present a software framework that can be used for the development of AR applications based on camera-projector pairs, that is suitable for both fixed, and nomadic setups.Peer ReviewedPostprint (author's final draft

Overview of open source augmented reality toolkit

Augmented reality or also known as AR is not a new technology. The technology has existed for almost 40 years ago after Ivan Sutherland introduced the first virtual reality (VR) application. At that time, works and research were mainly concerned to establish the hardware aspects of the technology. The head-mounted display (HMD) or some might called head-worn display is the result of augmented reality research and also one of the fundamental equipment for accessing the technology. As time goes by, the augmented reality technology has begin to mature to a point where the hardware cost and capabilities have collided to deliver a more feasible AR thus enable the rapid development of AR applications in many fields including education. To create a non-commercial AR application specifically for education, the ARToolkit can be taken into consideration. ARToolkit is the product of AR community and it is registered under the GNU General Public License. The user is provided with basic source code that lets the user easily develop Augmented Reality applications. Despite the fact that AR is not a new technology, people may unaware or unfamiliar with its existence. Therefore this paper is intended to (1) give an overview of augmented reality; and provides (2) solution to the technical problems that one’s will face in setting up open-source augmented reality toolkit

A tracker alignment framework for augmented reality

To achieve accurate registration, the transformations which locate the tracking system components with respect to the environment must be known. These transformations relate the base of the tracking system to the virtual world and the tracking system's sensor to the graphics display. In this paper we present a unified, general calibration method for calculating these transformations. A user is asked to align the display with objects in the real world. Using this method, the sensor to display and tracker base to world transformations can be determined with as few as three measurements

Spatial calibration of an optical see-through head-mounted display

We present here a method for calibrating an optical see-through Head Mounted Display (HMD) using techniques usually applied to camera calibration (photogrammetry). Using a camera placed inside the HMD to take pictures simultaneously of a tracked object and features in the HMD display, we could exploit established camera calibration techniques to recover both the intrinsic and extrinsic properties of the~HMD (width, height, focal length, optic centre and principal ray of the display). Our method gives low re-projection errors and, unlike existing methods, involves no time-consuming and error-prone human measurements, nor any prior estimates about the HMD geometry

Negotiating Reality

Our understanding of research through design is demonstrated by a close examination of the methods used in the project lifeClipper2. This design research project investigates the applicability of immersive outdoor Augmented Reality (AR). lifeClipper2 offers an audiovisual walking experience in a virtually extended public space and focuses on audiovisual perception as well as on the development of the appropriate technology. The project involves contributions of partners from different fields of research. Thus, lifeClipper2 is able to test the potential of AR for visualizing architecture and archaeological information and to challenge our understanding of perception and interaction. Using examples from our research, the paper reflects on how scenario design contributes to the production of design knowledge and explores the possibilities and variations of AR. Finally, the paper drafts our approach to design research. The three tenets of our work are: the use of scenarios as a tool of interdisciplinary research, the experimental exploration of media and the intention to make design knowledge explicit. Keywords: augmented reality; locative media; hybrid environment; immersion; perception; experience design; research through design; scenario design</p

An automated calibration method for non-see-through head mounted displays

Accurate calibration of a head mounted display (HMD) is essential both for research on the visual system and for realistic interaction with virtual objects. Yet, existing calibration methods are time consuming and depend on human judgements, making them error prone, and are often limited to optical see-through HMDs. Building on our existing approach to HMD calibration Gilson et al. (2008), we show here how it is possible to calibrate a non-see-through HMD. A camera is placed inside a HMD displaying an image of a regular grid, which is captured by the camera. The HMD is then removed and the camera, which remains fixed in position, is used to capture images of a tracked calibration object in multiple positions. The centroids of the markers on the calibration object are recovered and their locations re-expressed in relation to the HMD grid. This allows established camera calibration techniques to be used to recover estimates of the HMD display's intrinsic parameters (width, height, focal length) and extrinsic parameters (optic centre and orientation of the principal ray). We calibrated a HMD in this manner and report the magnitude of the errors between real image features and reprojected features. Our calibration method produces low reprojection errors without the need for error-prone human judgements