Handheld Guides in Inspection Tasks : Augmented Reality versus Picture

Inspection tasks focus on observation of the environment and are required in many industrial domains. Inspectors usually execute these tasks by using a guide such as a paper manual, and directly observing the environment. The effort required to match the information in a guide with the information in an environment and the constant gaze shifts required between the two can severely lower the work efficiency of inspector in performing his/her tasks. Augmented reality (AR) allows the information in a guide to be overlaid directly on an environment. This can decrease the amount of effort required for information matching, thus increasing work efficiency. AR guides on head-mounted displays (HMDs) have been shown to increase efficiency. Handheld AR (HAR) is not as efficient as HMD-AR in terms of manipulability, but is more practical and features better information input and sharing capabilities. In this study, we compared two handheld guides: an AR interface that shows 3D registered annotations, that is, annotations having a fixed 3D position in the AR environment, and a non-AR picture interface that displays non-registered annotations on static images. We focused on inspection tasks that involve high information density and require the user to move, as well as to perform several viewpoint alignments. The results of our comparative evaluation showed that use of the AR interface resulted in lower task completion times, fewer errors, fewer gaze shifts, and a lower subjective workload. We are the first to present findings of a comparative study of an HAR and a picture interface when used in tasks that require the user to move and execute viewpoint alignments, focusing only on direct observation. Our findings can be useful for AR practitioners and psychology researchers

Visual vs auditory augmented reality for indoor guidance

Indoor navigation systems are not widely used due to the lack of effective indoor tracking technology. Augmented Reality (AR) is a natural medium for presenting information in indoor navigation tools. However, augmenting the environment with visual stimuli may not always be the most appropriate method to guide users, e.g., when they are performing some other visual task or they suffer from visual impairments. This paper presents an AR app to support visual and auditory stimuli that we have developed for indoor guidance. A study (N=20) confirms that the participants reached the target when using two types of stimuli, visual and auditory. The AR visual stimuli outperformed the auditory stimuli in terms of time and overall distance travelled. However, the auditory stimuli forced the participants to pay more attention, and this resulted in better memorization of the route. These performance outcomes were independent of gender and age. Therefore, in addition to being easy to use, auditory stimuli promote route retention and show potential in situations in which vision cannot be used as the primary sensory channel or when spatial memory retention is important. We also found that perceived physical and mental efforts affect the subjective perception about the AR guidance app

SiTAR: Situated Trajectory Analysis for In-the-Wild Pose Error Estimation

Virtual content instability caused by device pose tracking error remains a prevalent issue in markerless augmented reality (AR), especially on smartphones and tablets. However, when examining environments which will host AR experiences, it is challenging to determine where those instability artifacts will occur; we rarely have access to ground truth pose to measure pose error, and even if pose error is available, traditional visualizations do not connect that data with the real environment, limiting their usefulness. To address these issues we present SiTAR (Situated Trajectory Analysis for Augmented Reality), the first situated trajectory analysis system for AR that incorporates estimates of pose tracking error. We start by developing the first uncertainty-based pose error estimation method for visual-inertial simultaneous localization and mapping (VI-SLAM), which allows us to obtain pose error estimates without ground truth; we achieve an average accuracy of up to 96.1% and an average F1 score of up to 0.77 in our evaluations on four VI-SLAM datasets. Next we present our SiTAR system, implemented for ARCore devices, combining a backend that supplies uncertainty-based pose error estimates with a frontend that generates situated trajectory visualizations. Finally, we evaluate the efficacy of SiTAR in realistic conditions by testing three visualization techniques in an in-the-wild study with 15 users and 13 diverse environments; this study reveals the impact both environment scale and the properties of surfaces present can have on user experience and task performance.Comment: To appear in Proceedings of IEEE ISMAR 202

Ambient Intelligence for Next-Generation AR

Next-generation augmented reality (AR) promises a high degree of context-awareness - a detailed knowledge of the environmental, user, social and system conditions in which an AR experience takes place. This will facilitate both the closer integration of the real and virtual worlds, and the provision of context-specific content or adaptations. However, environmental awareness in particular is challenging to achieve using AR devices alone; not only are these mobile devices' view of an environment spatially and temporally limited, but the data obtained by onboard sensors is frequently inaccurate and incomplete. This, combined with the fact that many aspects of core AR functionality and user experiences are impacted by properties of the real environment, motivates the use of ambient IoT devices, wireless sensors and actuators placed in the surrounding environment, for the measurement and optimization of environment properties. In this book chapter we categorize and examine the wide variety of ways in which these IoT sensors and actuators can support or enhance AR experiences, including quantitative insights and proof-of-concept systems that will inform the development of future solutions. We outline the challenges and opportunities associated with several important research directions which must be addressed to realize the full potential of next-generation AR.Comment: This is a preprint of a book chapter which will appear in the Springer Handbook of the Metavers

Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and perform seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences using MAR devices to provide universal access to digital content. Over the past 20 years, several MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discuss the latest studies on MAR through a top-down approach: (1) MAR applications; (2) MAR visualisation techniques adaptive to user mobility and contexts; (3) systematic evaluation of MAR frameworks, including supported platforms and corresponding features such as tracking, feature extraction, and sensing capabilities; and (4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields and the current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.Peer reviewe

Real-time single image depth perception in the wild with handheld devices

Depth perception is paramount to tackle real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image represents the most versatile solution, since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit its practical deployment: i) the low reliability when deployed in-the-wild and ii) the demanding resource requirements to achieve real-time performance, often not compatible with such devices. Therefore, in this paper, we deeply investigate these issues showing how they are both addressable adopting appropriate network design and training strategies -- also outlining how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time depth-aware augmented reality and image blurring with smartphones in-the-wild.Comment: 11 pages, 9 figure

Integration of digital twin to augmented reality in industrial enrichment plants:an anticipated user experience study

Abstract. Understanding user experience is necessary in developmental phases of every service, program and system. However, when adopting new technology, the early developmental phases methods and metrics used in user experience are not sufficient in anticipation of situational use and use cases of users. Therefore, focus and perspective to anticipated user experience research early in the development phase is needed. These problems are also true in ubiquitous computing field of Industry 4.0 where human computer interaction is in constant flux. New types of technologies and solutions are emerging and constantly seeking ways to capture their market share, bring added value or to revolutionize the industrial field without first understanding the anticipated requirements. This is true also for different industrial process control applications like digital twins and ways that augmented reality could be utilized in the on-site and monitoring operations. This thesis determines what the anticipated user experiences are for the professionals working in the industry and how applications can be integrated to augmented reality in industrial contexts. To tackle this issues artifact and gamification as design method will be utilized. Anticipated user experience study approach is applied and combined with early developmental artifact in the interviews. Research data is collected both qualitatively and quantitatively, but the main research method and data analysis is implemented with qualitative data. Contributions of this thesis is to determine if the use of early developmental artifact helps with the anticipations of user experiences and use cases for new technology adaptations to augmented reality in industrial contexts. This thesis proposes the term artifact specificity constraint and contributes to a model for converged relations of virtual reality continuum and digital twins. This thesis outlays a coding scheme with nine categories to be used as a reference for similar focused research