Subjective Evaluation of a Semi-Automatic Optical See-Through Head-Mounted Display Calibration Technique

In-Depth Review of Augmented Reality: Tracking Technologies, Development Tools, AR Displays, Collaborative AR, and Security Concerns

Augmented reality (AR) has gained enormous popularity and acceptance in the past few years. AR is indeed a combination of different immersive experiences and solutions that serve as integrated components to assemble and accelerate the augmented reality phenomena as a workable and marvelous adaptive solution for many realms. These solutions of AR include tracking as a means for keeping track of the point of reference to make virtual objects visible in a real scene. Similarly, display technologies combine the virtual and real world with the user's eye. Authoring tools provide platforms to develop AR applications by providing access to low-level libraries. The libraries can thereafter interact with the hardware of tracking sensors, cameras, and other technologies. In addition to this, advances in distributed computing and collaborative augmented reality also need stable solutions. The various participants can collaborate in an AR setting. The authors of this research have explored many solutions in this regard and present a comprehensive review to aid in doing research and improving different business transformations. However, during the course of this study, we identified that there is a lack of security solutions in various areas of collaborative AR (CAR), specifically in the area of distributed trust management in CAR. This research study also proposed a trusted CAR architecture with a use-case of tourism that can be used as a model for researchers with an interest in making secure AR-based remote communication sessions.

AR-Loupe: Magnified Augmented Reality by Combining an Optical See-Through Head-Mounted Display and a Loupe

Head-mounted loupes can increase the user's visual acuity to observe the details of an object. On the other hand, optical see-through head-mounted displays (OST-HMD) are able to provide virtual augmentations registered with real objects. In this article, we propose AR-Loupe, combining the advantages of loupes and OST-HMDs, to offer augmented reality in the user's magnified field-of-vision. Specifically, AR-Loupe integrates a commercial OST-HMD, Magic Leap One, and binocular Galilean magnifying loupes, with customized 3D-printed attachments. We model the combination of user's eye, screen of OST-HMD, and the optical loupe as a pinhole camera. The calibration of AR-Loupe involves interactive view segmentation and an adapted version of stereo single point active alignment method (Stereo-SPAAM). We conducted a two-phase multi-user study to evaluate AR-Loupe. The users were able to achieve sub-millimeter accuracy ( 0.82 mm) on average, which is significantly ( ) smaller compared to normal AR guidance ( 1.49 mm). The mean calibration time was 268.46 s. With the increased size of real objects through optical magnification and the registered augmentation, AR-Loupe can aid users in high-precision tasks with better visual acuity and higher accuracy.

The Influence of Label Design on Search Performance and Noticeability in Wide Field of View Augmented Reality Displays

In Augmented Reality (AR), search performance for outdoor tasks is an important metric for evaluating the success of a large number of AR applications. Users must be able to find content quickly, labels and indicators must not be invasive but still clearly noticeable, and the user interface should maximize search performance in a variety of conditions. To address these issues, we have set up a series of experiments to test the influence of virtual characteristics such as color, size, and leader lines on the performance of search tasks and noticeability in both real and simulated environments. We evaluate two primary areas, including 1) the effects of peripheral field of view (FOV) limitations and labeling techniques on target acquisition during outdoor mobile search, and 2) the influence of local characteristics such as color, size, and motion on text labels over dynamic backgrounds. The first experiment showed that limited FOV will severely limit search performance, but that appropriate placement of labels and leaders within the periphery can alleviate this problem without interfering with walking or decreasing user comfort. In the second experiment, we found that different types of motion are more noticeable in optical versus video see-through displays, but that blue coloration is most noticeable in both. Results can aid in designing more effective view management techniques, especially for wider field of view displays.

A Survey of Calibration Methods for Optical See-Through Head-Mounted Displays

Optical see-through head-mounted displays (OST HMDs) are a major output medium for Augmented Reality, which have seen significant growth in popularity and usage among the general public due to the growing release of consumer-oriented models, such as the Microsoft Hololens. Unlike Virtual Reality headsets, OST HMDs inherently support the addition of computer-generated graphics directly into the light path between a user's eyes and their view of the physical world. As with most Augmented and Virtual Reality systems, the physical position of an OST HMD is typically determined by an external or embedded 6-Degree-of-Freedom tracking system. However, in order to properly render virtual objects, which are perceived as spatially aligned with the physical environment, it is also necessary to accurately measure the position of the user's eyes within the tracking system's coordinate frame. For over 20 years, researchers have proposed various calibration methods to determine this needed eye position. However, to date, there has not been a comprehensive overview of these procedures and their requirements. Hence, this paper surveys the field of calibration methods for OST HMDs. Specifically, it provides insights into the fundamentals of calibration techniques, and presents an overview of both manual and automatic approaches, as well as evaluation methods and metrics. Finally, it also identifies opportunities for future research.

Towards Pervasive Augmented Reality: Context-Awareness in Augmented Reality

Augmented Reality is a technique that enables users to interact with their physical environment through the overlay of digital information. While being researched for decades, more recently, Augmented Reality moved out of the research labs and into the field. While most of the applications are used sporadically and for one particular task only, current and future scenarios will provide a continuous and multi-purpose user experience. Therefore, in this paper, we present the concept of Pervasive Augmented Reality, aiming to provide such an experience by sensing the user's current context and adapting the AR system based on the changing requirements and constraints. We present a taxonomy for Pervasive Augmented Reality and context-aware Augmented Reality, which classifies context sources and context targets relevant for implementing such a context-aware, continuous Augmented Reality experience. We further summarize existing approaches that contribute towards Pervasive Augmented Reality. Based our taxonomy and survey, we identify challenges for future research directions in Pervasive Augmented Reality.

Light-Field Correction for Spatial Calibration of Optical See-Through Head-Mounted Displays

A critical requirement for AR applications with Optical See-Through Head-Mounted Displays (OST-HMD) is to project 3D information correctly into the current viewpoint of the user - more particularly, according to the user's eye position. Recently-proposed interaction-free calibration methods [16], [17] automatically estimate this projection by tracking the user's eye position, thereby freeing users from tedious manual calibrations. However, the method is still prone to contain systematic calibration errors. Such errors stem from eye-/HMD-related factors and are not represented in the conventional eye-HMD model used for HMD calibration. This paper investigates one of these factors - the fact that optical elements of OST-HMDs distort incoming world-light rays before they reach the eye, just as corrective glasses do. Any OST-HMD requires an optical element to display a virtual screen. Each such optical element has different distortions. Since users see a distorted world through the element, ignoring this distortion degenerates the projection quality. We propose a light-field correction method, based on a machine learning technique, which compensates the world-scene distortion caused by OST-HMD optics. We demonstrate that our method reduces the systematic error and significantly increases the calibration accuracy of the interaction-free calibration.