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Proceedings Paper

Situational awareness for unmanned ground vehicles in semi-structured environments
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Paper Abstract

Situational Awareness (SA) is a critical component of effective autonomous vehicles, reducing operator workload and allowing an operator to command multiple vehicles or simultaneously perform other tasks. Our Scene Estimation & Situational Awareness Mapping Engine (SESAME) provides SA for mobile robots in semi-structured scenes, such as parking lots and city streets. SESAME autonomously builds volumetric models for scene analysis. For example, a SES-AME equipped robot can build a low-resolution 3-D model of a row of cars, then approach a specific car and build a high-resolution model from a few stereo snapshots. The model can be used onboard to determine the type of car and locate its license plate, or the model can be segmented out and sent back to an operator who can view it from different viewpoints. As new views of the scene are obtained, the model is updated and changes are tracked (such as cars arriving or departing). Since the robot's position must be accurately known, SESAME also has automated techniques for deter-mining the position and orientation of the camera (and hence, robot) with respect to existing maps. This paper presents an overview of the SESAME architecture and algorithms, including our model generation algorithm.

Paper Details

Date Published: 17 July 2002
PDF: 11 pages
Proc. SPIE 4715, Unmanned Ground Vehicle Technology IV, (17 July 2002); doi: 10.1117/12.474448
Show Author Affiliations
Thomas G. Goodsell, Charles River Analytics Inc. (United States)
Magnus Snorrason, Charles River Analytics Inc. (United States)
Mark R. Stevens, Charles River Analytics Inc. (United States)


Published in SPIE Proceedings Vol. 4715:
Unmanned Ground Vehicle Technology IV
Grant R. Gerhart; Chuck M. Shoemaker; Douglas W. Gage, Editor(s)

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