Moving object prediction for off-road autonomous navigation

Raj Madhavan; Craig I Schlenoff

doi:10.1117/12.485771

30 September 2003 Moving object prediction for off-road autonomous navigation

Raj Madhavan, Craig I Schlenoff

Proceedings Volume 5083, Unmanned Ground Vehicle Technology V; (2003) https://doi.org/10.1117/12.485771
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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.

Citation Download Citation

Raj Madhavan and Craig I Schlenoff "Moving object prediction for off-road autonomous navigation", Proc. SPIE 5083, Unmanned Ground Vehicle Technology V, (30 September 2003); https://doi.org/10.1117/12.485771

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