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Accurate Rough Terrain Estimation with Space-Carving Kernels

Raia Hadsell, J. Andrew (Drew) Bagnell, Daniel Huber and Martial Hebert
Conference Paper, Carnegie Mellon University, Proc. Robotics Science and Systems, June, 2009

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Abstract

Accurate terrain estimation is critical for autonomous offroad navigation. Reconstruction of a 3D surface allows rough and hilly ground to be represented, yielding faster driving and better planning and control. However, data from a 3D sensor samples the terrain unevenly, quickly becoming sparse at longer ranges and containing large voids because of occlusions and inclines. The proposed approach uses online kernel-based learning to estimate a continuous surface over the area of interest while providing upper and lower bounds on that surface. Unlike other approaches, visibility information is exploited to constrain the terrain surface and increase precision, and an efficient gradient-based optimization allows for realtime implementation.

BibTeX Reference
@conference{Hadsell-2009-10241,
title = {Accurate Rough Terrain Estimation with Space-Carving Kernels},
author = {Raia Hadsell and J. Andrew (Drew) Bagnell and Daniel Huber and Martial Hebert},
booktitle = {Proc. Robotics Science and Systems},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2009},
address = {Pittsburgh, PA},
}
2017-09-13T10:41:09+00:00