A tracker alignment framework for augmented reality

Baillot, Y.; Julier, Simon; Brown, D.; Livingston, Mark

doi:10.1109/ismar.2003.1240697

Cited by 18 publications

(18 citation statements)

References 14 publications

(26 reference statements)

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“…The user's position and orientation are overlaid on the photograph, and the user is able to modify the displayed orientation to directly coincide with the user's chosen feature. This calibration procedure is similar to the single point calibration technique formalized by Baillot et al [3], except that in our system the calibration location does not have to be predefined because the user chooses the necessary points on the aerial photograph during the process. However, because the user input is limited to adjusting the orientation in the overhead view, only error in yaw is accounted for.…”

Section: Calibrationmentioning

confidence: 99%

Using aerial photographs for improved mobile AR annotation

Wither

DiVerdi

Höllerer

2006

2006 IEEE/ACM International Symposium on Mixed and Augmented Reality

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We present a mobile augmented reality system for outdoor annotation of the real world. To reduce user burden, we use aerial photographs in addition to the wearable system's usual data sources (position, orientation, camera and user input). This allows the user to accurately annotate 3D features with only a few simple interactions from a single position by aligning features in both their firstperson viewpoint and in the aerial view. We examine three types of aerial photograph features -corners, edges, and regions -that are suitable for a wide variety of useful mobile augmented reality applications, and are easily visible on aerial photographs. By using aerial photographs in combination with wearable augmented reality, we are able to achieve much higher accuracy 3D annotation positions than was previously possible from a single user location.

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Section: Calibrationmentioning

confidence: 99%

Using aerial photographs for improved mobile AR annotation

Wither

DiVerdi

Höllerer

2006

2006 IEEE/ACM International Symposium on Mixed and Augmented Reality

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“…We have typically assumed that perfect registration would be achieved, and thus have often drawn geometric structures in great detail, only to have them wander far from the proper locations ( Figure 1). There have been attempts to reduce registration error through improved tracking and calibration algorithms [1,2,19] and intelligent architectures [5,9]. Still the problem persists for many AR implementations.…”

Section: Introductionmentioning

confidence: 99%

The effect of registration error on tracking distant augmented objects

Livingston

2008

2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality

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We conducted a user study of the effect of registration error on performance of tracking distant objects in augmented reality. Categorizing error by types that are often used as specifications, we hoped to derive some insight into the ability of users to tolerate noise, latency, and orientation error. We used measurements from actual systems to derive the parameter settings. We expected all three errors to influence users' ability to perform the task correctly and the precision with which they performed the task. We found that high latency had a negative impact on both performance and response time. While noise consistently interacted with the other variables, and orientation error increased user error, the differences between "high" and "low" amounts were smaller than we expected. Results of users' subjective rankings of these three categories of error were surprisingly mixed. Users believed noise was the most detrimental, though statistical analysis of performance refuted this belief. We interpret the results and draw insights for system design.

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“…However, many tracking systems only provide performance specifications and the covariances must be approximated from these. 2 Calibration. An AR system consists of a tracking system and a display system and the calibration of these systems and the relationship between them must be known.…”

Section: Sources Of Uncertaintymentioning

confidence: 99%

“…However, there is no guarantee that these parameters are correct for a moving camera in a scene with different camera settings. The problems are exacerbated in see-through AR systems because current methods require the user to align objects on the display with those in the physical world (e.g., SPAAM [14] or the alignment framework described in [2]). Issues such as fatigue, the finite sampling space and user error can lead to inaccuracies.…”

Section: Sources Of Uncertaintymentioning

confidence: 99%