The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.

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RADAR: an in-building RF-based user location and tracking system

This paper surveys the field of augmented reality AR, in which 3D virtual objects are integrated into a 3D real environment in real time. It describes the medical, manufacturing, visualization, path planning, entertainment, and military applications that have been explored. This paper describes the characteristics of augmented reality systems, including a detailed discussion of the tradeoffs between optical and video blending approaches. Registration and sensing errors are two of the biggest problems in building effective augmented reality systems, so this paper summarizes current efforts to overcome these problems. Future directions and areas requiring further research are discussed. This survey provides a starting point for anyone interested in researching or using augmented reality.

/pdf/a-survey-of-augmented-reality-17irtaxt7z.pdf

A survey of augmented reality

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

/pdf/computer-vision-algorithms-and-applications-25dn6wu83j.pdf

Computer Vision: Algorithms and Applications

Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, lightweight, untethered, and unobtrusive. The problem of localization, that is, determining where a given node is physically located in a network, is a challenging one, and yet extremely crucial for many of these applications. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the reliance on GPS of all nodes in these networks. We review localization techniques and evaluate the effectiveness of a very simple connectivity metric method for localization in outdoor environments that makes use of the inherent RF communications capabilities of these devices. A fixed number of reference points in the network with overlapping regions of coverage transmit periodic beacon signals. Nodes use a simple connectivity metric, which is more robust to environmental vagaries, to infer proximity to a given subset of these reference points. Nodes localize themselves to the centroid of their proximate reference points. The accuracy of localization is then dependent on the separation distance between two-adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90 percent of our data points is within one-third of the separation distance. However, future work is needed to extend the technique to more cluttered environments.

/pdf/gps-less-low-cost-outdoor-localization-for-very-small-3cpquwbe1v.pdf

GPS-less low-cost outdoor localization for very small devices

In 1997, Azuma published a survey on augmented reality (AR). Our goal is to complement, rather than replace, the original survey by presenting representative examples of the new advances. We refer one to the original survey for descriptions of potential applications (such as medical visualization, maintenance and repair of complex equipment, annotation, and path planning); summaries of AR system characteristics (such as the advantages and disadvantages of optical and video approaches to blending virtual and real, problems in display focus and contrast, and system portability); and an introduction to the crucial problem of registration, including sources of registration error and error-reduction strategies.

https://www.cc.gatech.edu/~blair/papers/ARsurveyCGA.pdf

Recent advances in augmented reality

In Augmented Reality, see-through HMDs superimpose virtual 3D objects on the real world. This technology has the potential to enhance a user's perception and interaction with the real world. However, many Augmented Reality applications will not be accepted until we can accurately register virtual objects with their real counterparts. In previous systems, such registration was achieved only from a limited range of viewpoints, when the user kept his head still. This paper offers improved registration in two areas. First, our system demonstrates accurate static registration across a wide variety of viewing angles and positions. An optoelectronic tracker provides the required range and accuracy. Three calibration steps determine the viewing parameters. Second, dynamic errors that occur when the user moves his head are reduced by predicting future head locations. Inertial sensors mounted on the HMD aid head-motion prediction. Accurate determination of prediction distances requires low-overhead operating systems and eliminating unpredictable sources of latency. On average, prediction with inertial sensors produces errors 2-3 times lower than prediction without inertial sensors and 5-10 times lower than using no prediction at all. Future steps that may further improve registration are outlined.

/pdf/improving-static-and-dynamic-registration-in-an-optical-see-5805lmhp50.pdf

Improving static and dynamic registration in an optical see-through HMD

The biggest single obstacle to building effective augmented reality (AR) systems is the lack of accurate wide-area sensors for trackers that report the locations and orientations of objects in an environment. Active (sensor-emitter) tracking technologies require powered-device installation. Limiting their use to prepared areas that are relatively free of natural or man-made interference sources. Vision-based systems can use passive landmarks, but they are more computationally demanding and often exhibit erroneous behavior due to occlusion or numerical instability. Inertial sensors are completely passive, requiring no external devices or targets, however, the drift rates in portable strapdown configurations are too great for practical use. In this paper, we present a hybrid approach to AR tracking that integrates inertial and vision-based technologies. We exploit the complementary nature of the two technologies to compensate for the weaknesses in each component. Analysis and experimental results demonstrate this system's effectiveness.

https://www.ronaldazuma.com/papers/vr99_2.pdf

Hybrid inertial and vision tracking for augmented reality registration

I In this Issue, Fltzmaurlc,~ = and Feiner describe two different augmented-reality systems. Such sy~stems require highly capable head and object trackers to create an effective Illusion of virtual objects coexisting with the real world. For ordinary virtual environments that completely replace the real world with a virtual world, It sufflo~=s to know the approximate position and orientation of the user's head. Small errors are not easily discernible because the user's visual sense tencls to override 1:he conflictIng signals from his or her w~=stlbular and broprloceptlve systems. But In augmented reality, virtual objec\"ts supplement rather than supplant tl~e real world. Preserving the Illusion that the two coexist requires proper alignment and reglstral~lon of the vlrtu~al objects to the real world. Even tiny errors in regis-tratlon are easily detectable by the human visual system. What does augmented reality require from trackers to avoid such errors? First, a tracker must be accurate to a small fraction of a degree In orientation and a few millimeters (mm) in position. Figure 1. ,Conceptual ctrawing of sensors viiewing beacons In the ceiling Errors in measured head orientation usually cause larger registration offsets than object orientation errors do, making this requirement more critical for systems based on Head-Mounted Displays (HMDS). Try the following simple demonstration. Take out a dime and hold It at arm's length. The diameter of the dime covers approximately 1.5 degrees of arc. In comparison, a full moon covers 1/2 degree of arc. Now imagine a virtual coffee cup sitting on the corner of a real table two meters away from you. An angular error of 1.5 degrees in head orientation moves the cup by about 52 mm. Clearly, small orientation errors could result In a cup suspended in midair or interpene-trating the table. Similarly, If we want the cup to stay within 1 to 2 mm of Its true position, then we cannot tolerate tracker positional errors of more than 1 to 2 mm. Second, the combined latency of the tracker and the graphics engine must be very low. Combined latency is the delay from the time the tracker subsystem takes its measurements to the time the corresponding images appear In the display devices. Many HMD-based systems have a combined latency over 100 ms. At a moderate head or object rotation rate of 50 degrees per second, 100 milliseconds (ms) of latency causes 5 degrees of angular error. At a rapid rate …

Ronald Azuma

Papers

A survey of augmented reality

Recent advances in augmented reality

Improving static and dynamic registration in an optical see-through HMD

Hybrid inertial and vision tracking for augmented reality registration

Tracking requirements for augmented reality