19 July 1999 Registration for an augmented reality system enhancing the situational awareness in an outdoor scenario
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Proceedings Volume 3691, Enhanced and Synthetic Vision 1999; (1999); doi: 10.1117/12.354425
Event: AeroSense '99, 1999, Orlando, FL, United States
Abstract
Augmented reality techniques can significantly enhance the situational awareness of a user by providing 3D information registered to the user's view of the world. Critical for usability of such a system is the precise registration. Outdoor AR systems usually employ a GPS for position and a hybrid combination of magnetometer and inclinometer for orientation estimation, which provide only a limited precision. At the Rockwell Science Center (RSC), an approach is being developed which uses terrain horizon silhouettes as visual cues for improving the registration precision and for sensor calibration. Since the observer position is known from GPS data, the horizon silhouette can be predicted using a digital elevation model (DEM) database. The silhouette segment, which the user sees, is extracted from a camera pointing in the same direction as the user. This segment is then matched with the complete 360 degree horizon silhouette from the DEM data. When the optimal match has been determined, camera azimuth, roll, and pitch angle can be computed. This registration approach is being implemented in an AR system for enhancing the situational awareness in an outdoor scenario which is being developed at RSC. Currently, the information augmentation is provided by a registered overlay onto a live video stream.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Reinhold Behringer, "Registration for an augmented reality system enhancing the situational awareness in an outdoor scenario", Proc. SPIE 3691, Enhanced and Synthetic Vision 1999, (19 July 1999); doi: 10.1117/12.354425; https://doi.org/10.1117/12.354425
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KEYWORDS
Visualization

Cameras

Video

Calibration

Image registration

Autoregressive models

Situational awareness sensors

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