Enabling Self-aware Smart Buildings by Augmented Reality

Conventional HVAC control systems are usually incognizant of the physical structures and materials of buildings. These systems merely follow pre-set HVAC control logic based on abstract building thermal response models, which are rough approximations to true physical models, ignoring dynamic spatial variations in built environments. To enable more accurate and responsive HVAC control, this paper introduces the notion of "self-aware" smart buildings, such that buildings are able to explicitly construct physical models of themselves (e.g., incorporating building structures and materials, and thermal flow dynamics). The question is how to enable self-aware buildings that automatically acquire dynamic knowledge of themselves. This paper presents a novel approach using "augmented reality". The extensive user-environment interactions in augmented reality not only can provide intuitive user interfaces for building systems, but also can capture the physical structures and possibly materials of buildings accurately to enable real-time building simulation and control. This paper presents a building system prototype incorporating augmented reality, and discusses its applications.Comment: This paper appears in ACM International Conference on Future Energy Systems (e-Energy), 201

Quantitative evaluation of overlaying discrepancies in mobile augmented reality applications for AEC/FM

Augmented Reality (AR) is a trending technology that provides a live view of the real and physical environment augmented by virtual elements, enhancing the information of the scene with digital information (sound, video, graphics, text or geo-location). Its application to architecture, engineering and construction, and facility management (AEC/FM) is straightforward and can be very useful to improve the on-site work at different stages of the projects. However, one of the most important limitations of Mobile Augmented Reality (MAR) is the lack of accuracy when the screen overlays the virtual models on the real images captured by the camera. The main reasons are errors related to tracking (positioning and orientation of the mobile device) and image capture and processing (projection and distortion issues). This paper shows a new methodology to mathematically perform a quantitative evaluation, in world coordinates, of those overlaying discrepancies on the screen, obtaining the real-scale distances from any real point to the sightlines of its virtual projections for any AR application. Additionally, a new utility for filtering built-in sensor signals in mobile devices is presented: the Drift-Vibration-Threshold function (DVT), a straightforward tool to filter the drift suffered by most sensor-based tracking systems

Smart Photos

Recent technological leaps have been a great catalyst for changing how people interact with the world around us. Specifically, the field of Augmented Reality has led to many software and hardware advances that have formed a digital intermediary between humans and their environment. As of now, Augmented Reality is available to the select few with the means of obtaining Google Glass, Oculus Rifts, and other relatively expensive platforms. Be that as it may, the tech industry\u27s current goal has been integration of this technology into the public\u27s smartphones and everyday devices. One inhibitor of this goal is the difficulty of finding an Augmented Reality application whose usage could satisfy an everyday need or attraction. Augmented reality presents our world in a unique perspective that can be found nowhere else in the natural world. However, visual impact is weak without substance or meaning. The best technology is invisible, and what makes a good product is its ability to fill a void in a person\u27s life. The most important researchers in this field are those who have been augmenting the tasks that most would consider mundane, such as overlaying nutritional information directly onto a meal [4]. In the same vein, we hope to incorporate Augmented Reality into everyday life by unlocking the full potential of a technology often believed to have already have reached its peak. The humble photograph, a classic invention and unwavering enhancement to the human experience, captures moments in space and time and compresses them into a single permanent state. These two-dimensional assortments of pixels give us a physical representation of the memories we form in specific periods of our lives. We believe this representation can be further enhanced in what we like to call a Smart Photo. The idea behind a Smart Photo is to unlock the full potential in the way that people can interact with photographs. This same notion is explored in the field of Virtual Reality with inventions such as 3D movies, which provide a special appeal that ordinary 2D films cannot. The 3D technology places the viewer inside the film\u27s environment. We intend to marry this seemingly mutually exclusive dichotomy by processing 2D photos alongside their 3D counterparts

Mobile Augmented Reality: Applications and Spe-cific Technical Issues

DOI: 10.1007/978-3-319-04702-7 Print ISBN: 978-3-319-04701-0 Online ISBN: 978-3-319-04702-7Although human's sedentary nature over time, his wish to travel the world remains as strong as ever. This paper discusses how imagery and Augmented Reality (AR) techniques can be of great help not only when discovering a new urban environment but also when observ-ing the evolution of the natural environment. The study is applied on Smartphone which is currently our most familiar device. Smart phone is utilized in our daily lives because it is low weight, ease of communications, and other valuable applications. In this chapter, we discuss technical issues of augmented reality especially with building recognition. Our building recog-nition method is based on an efficient hybrid approach, which combines the potentials of Speeded Up Robust Features (SURF) features points and lines. Our method relies on Approxi-mate Nearest Neighbors Search approach (ANNS). Although ANNS approaches are high speed, they are less accurate than linear algorithms. To assure an optimal trade-off between speed and accuracy, the proposed method performs a filtering step on the top of the ANNS. Finally, our method calls Hausdorff measure [15] with line models