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1. Augmented reality-based vision-aid indoor navigation system in GPS denied environment

   https://doi.org/10.1117/12.2519224  

   Rajeev, Srijith; Wan, Qianwen; Yau, Kenny; Panetta, Karen; Agaian, Sos S. (May 2019, Mobile Multimedia/Image Processing, Security, and Applications)

   Agaian, Sos S.; DelMarco, Stephen P.; Asari, Vijayan K. (Ed.)

   High accuracy localization and user positioning tracking is critical in improving the quality of augmented reality environments. The biggest challenge facing developers is localizing the user based on visible surroundings. Current solutions rely on the Global Positioning System (GPS) for tracking and orientation. However, GPS receivers have an accuracy of about 10 to 30 meters, which is not accurate enough for augmented reality, which needs precision measured in millimeters or smaller. This paper describes the development and demonstration of a head-worn augmented reality (AR) based vision-aid indoor navigation system, which localizes the user without relying on a GPS signal. Commercially available augmented reality head-set allows individuals to capture the field of vision using the front-facing camera in a real-time manner. Utilizing captured image features as navigation-related landmarks allow localizing the user in the absence of a GPS signal. The proposed method involves three steps: a detailed front-scene camera data is collected and generated for landmark recognition; detecting and locating an individual’s current position using feature matching, and display arrows to indicate areas that require more data collects if needed. Computer simulations indicate that the proposed augmented reality-based vision-aid indoor navigation system can provide precise simultaneous localization and mapping in a GPS-denied environment. Keywords: Augmented-reality, navigation, GPS, HoloLens, vision, positioning system, localization 

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2. Quill: A Declarative Approach for Accelerating Augmented Reality Application Development

   Codi Burley, Ritesh Sarkhel (September 2022, A Quarterly bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering)

   Sudeepa Roy and Jun Yang (Ed.)

   Data we encounter in the real-world such as printed menus, business documents, and nutrition labels, are often ad-hoc. Valuable insights can be gathered from this data when combined with additional information. Recent advances in computer vision and augmented reality have made it possible to understand and enrich such data. Joining real-world data with remote data stores and surfacing those enhanced results in place, within an augmented reality interface can lead to better and more informed decision-making capabilities. However, building end-user applications that perform these joins with minimal human effort is not straightforward. It requires a diverse set of expertise, including machine learning, database systems, computer vision, and data visualization. To address this complexity, we present Quill – a framework to develop end-to-end applications that model augmented reality applications as a join between real- world data and remote data stores. Using an intuitive domain-specific language, Quill accelerates the development of end-user applications that join real-world data with remote data stores. Through experiments on applications from multiple different domains, we show that Quill not only expedites the process of development, but also allows developers to build applications that are more performant than those built using standard developer tools, thanks to the ability to optimize declarative specifications. We also perform a user-focused study to investigate how easy (or difficult) it is to use Quill for developing augmented reality applications than other existing tools. Our results show that Quill allows developers to build and deploy applications with a lower technical background than building the same application using existing developer tools. 

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3. Enhancing ground penetrating radar with augmented reality systems for underground utility management

   https://doi.org/10.1117/12.2561042  

   Childs, Joshua; Orfeo, Dan; Burns, Dylan; Huston, Dryver; Xia, Tian (April 2020, SPIE Proc Vol 11426-08, Virtual, Augmented, and Mixed Reality (XR) Technology for Multi-Domain Operations)

   Dennison, Mark S. (Ed.)

   Successful maintenance and development of underground infrastructures depends on the ability to access underground utilities efficiently. In general, obtaining accurate positions and conditions of subterranean utilities is not trivial due to inaccurate data records and occlusions that are common in densely populated urban areas. Limited access to underground resources poses challenges to underground utilities management. Ground penetrating radar (GPR) is an effective sensing tools widely used for underground sensing. Combining high accuracy GPR data and augmented reality (AR) poses enables accurate real time visualizations of the buried objects. Although GPR and AR collect and visualize high accuracy data, intensive computation is required. This work presents a novel GPR-AR system that decreases post-processing time significantly while maintaining a neutral format across GPR-AR data collection methods regardless of varying Internet or GPS connection strengths. The methods explored in this work to mitigate failures of previous systems include automated and georeferenced post processing, the classification of underground assets using artificial intelligence, and real time data collection path visualizations. This work also lays a foundation for the potential combinations of a 5G GPR-AR system in which the temporal gap between data collection and visualization can be alleviated. 

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4. Enabling human–infrastructure interfaces for inspection using augmented reality

   https://doi.org/10.1177/1475921720977017  

   Maharjan, D; Agüero, M; Mascarenas, D; Fierro, R; Moreu, F (July 2021, Structural Health Monitoring)

   Decaying infrastructure maintenance cost allocation depends heavily on accurate and safe inspection in the field. New tools to conduct inspections can assist in prioritizing investments in maintenance and repairs. The industrial revolution termed as “Industry 4.0” is based on the intelligence of machines working with humans in a collaborative workspace. Contrarily, infrastructure management has relied on the human for making day-to-day decisions. New emerging technologies can assist during infrastructure inspections, to quantify structural condition with more objective data. However, today’s owners agree in trusting the inspector’s decision in the field over data collected with sensors. If data collected in the field is accessible during the inspections, the inspector decisions can be improved with sensors. New research opportunities in the human–infrastructure interface would allow researchers to improve the human awareness of their surrounding environment during inspections. This article studies the role of Augmented Reality (AR) technology as a tool to increase human awareness of infrastructure in their inspection work. The domains of interest of this research include both infrastructure inspections (emphasis on the collection of data of structures to inform management decisions) and emergency management (focus on the data collection of the environment to inform human actions). This article describes the use of a head-mounted device to access real-time data and information during their field inspection. The authors leverage the use of low-cost smart sensors and QR code scanners integrated with Augmented Reality applications for augmented human interface with the physical environment. This article presents a novel interface architecture for developing Augmented Reality–enabled inspection to assist the inspector’s workflow in conducting infrastructure inspection works with two new applications and summarizes the results from various experiments. The main contributions of this work to computer-aided community are enabling inspectors to visualize data files from database and real-time data access using an Augmented Reality environment. 

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5. Inside the ice shelf: using augmented reality to visualise 3D lidar and radar data of Antarctica

   https://doi.org/10.1111/phor.12298  

   Boghosian, Alexandra L.; Pratt, Martin J.; Becker, Maya K.; Cordero, S. Isabel; Dhakal, Tejendra; Kingslake, Jonathan; Locke, Caitlin D.; Tinto, Kirsty J.; Bell, Robin E. (December 2019, The Photogrammetric Record)

   Abstract From 2015 to 2017, theROSETTA‐Ice project comprehensively mapped Antarctica's Ross Ice Shelf using IcePod, a newly developed aerogeophysical platform. The campaign imaged the ice‐shelf surface with lidar and its internal structure with ice‐penetrating radar. TheROSETTA‐Ice data was combined with pre‐existing ice surface and bed topography digital elevation models to create the first augmented reality (AR) visualisation of the Antarctic Ice Sheet, using the Microsoft HoloLens. TheROSETTA‐Ice datasets support cross‐disciplinary science that aims to understand 4D processes, namely the change of 3D ice‐shelf structures over time. The work presented here usesARto visualise this dataset in 3D and highlights howARcan be simultaneously a useful research tool for interdisciplinary geoscience as well as an effective device for science communication education. 

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