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Proceedings Paper

Moving object prediction for off-road autonomous navigation
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Paper Abstract

The realization of on- and off-road autonomous navigation of Unmanned Ground Vehicles (UGVs) requires real-time motion planning in the presence of dynamic objects with unknown trajectories. To successfully plan paths and to navigate in an unstructured environment, the UGVs should have the difficult and computationally intensive competency to predict the future locations of moving objects that could interfere with its path. This paper details the development of a combined probabilistic object classification and estimation theoretic framework to predict the future location of moving objects, along with an associated uncertainty measure. The development of a moving object testbed that facilitates the testing of different representations and prediction algorithms in an implementation-independent platform is also outlined.

Paper Details

Date Published: 30 September 2003
PDF: 12 pages
Proc. SPIE 5083, Unmanned Ground Vehicle Technology V, (30 September 2003); doi: 10.1117/12.485771
Show Author Affiliations
Raj Madhavan, National Institute of Standards and Technology (United States)
Craig I Schlenoff, National Institute of Standards and Technology (United States)


Published in SPIE Proceedings Vol. 5083:
Unmanned Ground Vehicle Technology V
Grant R. Gerhart; Charles M. Shoemaker; Douglas W. Gage, Editor(s)

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