Home/Space-carving Kernels for Accurate Rough Terrain Estimation

Space-carving Kernels for Accurate Rough Terrain Estimation

Raia Hadsell, J. Andrew (Drew) Bagnell, Daniel Huber and Martial Hebert
Carnegie Mellon University, International Journal of Robotics Research, Vol. 29, No. 8, pp. 981-996, July, 2010

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Accurate terrain estimation is critical for autonomous offroad navigation. Reconstruction of a three-dimensional (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. To model sensor noise over varying ranges, a non-stationary covariance function is adopted. Experimental results are presented for several datasets, including groundtruthed terrain and a large 3D stereo dataset.

BibTeX Reference
@article{Hadsell-2010-10490,
title = {Space-carving Kernels for Accurate Rough Terrain Estimation},
author = {Raia Hadsell and J. Andrew (Drew) Bagnell and Daniel Huber and Martial Hebert},
booktitle = {International Journal of Robotics Research},
school = {Robotics Institute , Carnegie Mellon University},
month = {July},
year = {2010},
volume = {29},
number = {8},
pages = {981-996},
address = {Pittsburgh, PA},
}
2017-09-13T10:40:40+00:00