Azuma, R., Predictive tracking for augmented reality, in Computer Science. 1995, University of North Carolina, Chapel Hill: Chapel Hill, NC. p. 262.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....using 120 160 sized images. The current bottleneck is the image scanning routine; hence the small image sizes. 8 Future work The primary bottleneck is the image scanning routine; a faster algorithm would allow us to use higherresolution images. We would also like to incorporate prediction [1], which would speed up both the 2D image scanning and the camera parameter search. More advanced prediction would also result in smoother tracking. ....

Ronald Tadao Azuma. Predictive tracking for augmented reality. Technical Report TR95-007, Univ. North Carolina, Chapel Hill, 22, 1995.

....as e.g. the work surface in the case of a mouse. But in many SDG systems such a surface may not be available everywhere in a meeting environment. To reduce the effects of jitter on the laser pointer position we use a Kalman filter to smooth out the readings similar to the approach described in [6]. With the described techniques we are able to reliably track a laser pointer with an NTSC camera at 60 Hz. If configured correctly a side benefit of the Kalman filter is that it smoothes out jitter due to the even and odd fields of the NTSC signal. 3.2 Selecting objects with laser pointers For ....

Azuma, R. T., Predictive Tracking for Augmented Reality, Ph.D. Dissertation, University of North Carolina at Chapel Hill, NC, USA.

....and provides structured access to the data through a device independent interface. A major part of the system is a data processing module (3) which improves information quality for the application (4) through the means of mathematical methods such as interpolation and extrapolation techniques [7]. Figure 3 gives a better understanding of data and control flow during the tracking process. As stated in [8] communication between two subsystems is either controlled by the sender s request (push style) or by the receiver s request (pull style) The push method empowers the supplier to send ....

R.T. Azuma. Predictive Tracking for Augmented Reality. PhD thesis, Dept. Computer Sciences, Univ. of North Carolina, Chapel Hill, 1995.

....magnetic position and orientation tracking system with a hypothetical SCAAT implementation. SCAAT Estimate x yzxyz 2.4 Data Fusion Hybrid Systems The Kalman filter [26] has been widely used for data fusion. For example in navigation systems [17,30] virtual environment tracking systems [5,12,14], and in 3D scene modeling [20,42] However the SCAAT method represents a new approach to Kalman filter based multi sensor data fusion. Because constraints are intentionally incorporated one at a time, one can pick and choose which ones to add, and when to add them. This means that information ....

....introductory discussion can be found in [40] which also contains some interesting historical narrative. More extensive references can be found in [7,18,24,28,31,46] The Kalman filter has been employed previously for virtual environment tracking estimation and prediction. For example see [2,5,12,14,42], and most recently [32] In each of these cases however the filter was applied directly and only to the 6D pose estimates delivered by the off the shelf tracker. The SCAAT approach could be applied to either a hybrid system using off theshelf and or custom trackers, or it could be employed by ....

Ronald Azuma. 1995. "Predictive Tracking for Augmented Reality, " Ph.D. dissertation, University of North Carolina at Chapel Hill, TR95-007.

....speed, resolution, accuracy, robustness, and flexibility. 1. INTRODUCTION Systems for head tracking for interactive computer graphics have been explored for over 30 years (Sutherland, 1968) As illustrated in Figure 1, the authors have been working on the problem for over twenty years (Azuma, 1993, 1995; Azuma Bishop, 1994a, 1994b; Azuma Ward, 1991; Bishop, 1984; Gottschalk Hughes, 1993; UNC Tracker Project, 2000; Wang, 1990; J. F. Wang et al. 1990; Ward, Azuma, Bennett, Gottschalk, Fuchs, 1992; Welch, 1995, 1996; Welch Bishop, 1997; Welch et al. 1999) From the beginning our efforts ....

....1986; Maybeck, 1979; Welch Bishop, 1995) Finally, we maintain a Kalman filter web page (Welch Bishop, 2000) with introductory, reference, and research material. The Kalman filter has been used previously to address similar or related problems. See for example (Azarbayejani Pentland, 1995; Azuma, 1995; Emura Tachi, 1994; Fuchs (Foxlin) 1993; Mazuryk Gervautz, 1995; Van Pabst Krekel, 1993) A relevant example of a Kalman filter used for sensor fusion in wide area tracking system is given by (Foxlin et al. 1998) which describes a hybrid inertial acoustic system that is ....

Azuma, R. T. (1995). Predictive Tracking for Augmented Reality. Unpublished Ph.D. Dissertation, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.

....not timely, then there will be relative latency between the apparent motion of the real world and the apparent motion of the synthetic imagery. This type of latency can be managed [Jacobs97] by calibration and techniques such as interleaving data acquisition and computation, predictive tracking [Azuma95] and in cases where the need for spatial alignment justifies allowing delay, by interpolating past data [Jacobs97] Delay in presentation of images and in tracking subsystems can be very disturbing to the user, to the point 20 of inducing physical sickness. Users can tolerate some latency, ....

....When used in combination with another technology, inertial sensors provide a fast and accurate system. Such multi sensor tracking systems are discussed in Section 2.2.3.4. Because they directly measure velocity and or acceleration, inertial sensors are well suited to predictive tracking schemes [Azuma95] The major drawback of inertial sensors is that they can also make relative measurements. Thus they accumulate error over time, and appear to drift in space. The acceleration due to gravity must be subtracted from the readings of an accelerometer. This is non trivial; it requires calibration ....

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Azuma, R. T. (1995). Predictive Tracking for Augmented Reality. Ph.D. Dissertation, University of North Carolina at Chapel Hill.

....and display resolution do not seem to impact user performance as profoundly as latency [Ellis99] In augmented reality, the system latency has even more impact on the quality of the virtual experience. Latency will make the virtual objects appear to swim around and lag behind real objects [Azuma95]. A prerequisite to reducing system latency is to have a convenient method of measuring it. The system end to end latency is the time difference between a user input to a system and the display of the system s response to that input. It can be the time delay from when the user moves the ....

Ronald T. Azuma. Predictive Tracking for Augmented Reality. Doctoral Thesis, University of North Carolina, February 1995.

....[17] while a more complete introductory discussion can be found in [20] which also contains some interesting historical narrative. More extensive references can be found in [7, 12, 14, 16, 17, 30] The Kalman filter has been used previously to address similar or related problems. See for example [2, 3, 9, 10, 18, 23], and most recently [11] The SCAAT approach in particular is described in great detail in [28, 29] The benefits of using this approach, as opposed to a multiple constraint approach such as [4] are also discussed in [28, 29] However one key benefit warrants discussion here. There is a direct ....

Azuma, Ronald. 1995. "Predictive Tracking for Augmented Reality," Ph.D. dissertation, University of North Carolina at Chapel Hill, TR95-007.

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Azuma, R., Predictive tracking for augmented reality, in Computer Science. 1995, University of North Carolina, Chapel Hill: Chapel Hill, NC. p. 262.

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R. Azuma, "Predictive tracking for augmented reality", TR95-007, UNC-Chapel Hill, February 1995.

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Azuma, R., Predictive Tracking for Augmented Reality, Ph.D. Dissertation, University of North Carolina at Chapel Hill, Computer Science Technical Report TR#95-007, Chapel Hill, NC, February 1995.

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Azuma95 Azuma, R. Predictive Tracking for Augmented Reality. Ph.D. dissertation, The University of North Carolina at Chapel Hill, TR95-007 (1995).

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Azuma95 Azuma, Ronald. 1995. "Predictive Tracking for Augmented Reality, " Ph.D. dissertation, University of North Carolina at Chapel Hill, TR95-007.

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Azuma, Ronald. 1995. "Predictive Tracking for Augmented Reality," Ph.D. dissertation, University of North Carolina at Chapel Hill, TR95-007.

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