A near-to-far non-parametric learning approach for estimating traversability in deformable terrain

Ken, Ho, Peynot, Thierry, & Sukkarieh, Salah (2013) A near-to-far non-parametric learning approach for estimating traversability in deformable terrain. In Proceedings of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Tokyo Big Sight, Tokyo, pp. 2827-2833.

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It is well recognized that many scientifically interesting sites on Mars are located in rough terrains. Therefore, to enable safe autonomous operation of a planetary rover during exploration, the ability to accurately estimate terrain traversability is critical. In particular, this estimate needs to account for terrain deformation, which significantly affects the vehicle attitude and configuration. This paper presents an approach to estimate vehicle configuration, as a measure of traversability, in deformable terrain by learning the correlation between exteroceptive and proprioceptive information in experiments. We first perform traversability estimation with rigid terrain assumptions, then correlate the output with experienced vehicle configuration and terrain deformation using a multi-task Gaussian Process (GP) framework. Experimental validation of the proposed approach was performed on a prototype planetary rover and the vehicle attitude and configuration estimate was compared with state-of-the-art techniques. We demonstrate the ability of the approach to accurately estimate traversability with uncertainty in deformable terrain.

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ID Code: 67654
Item Type: Conference Paper
Refereed: Yes
Additional Information: 14002165
near-to-far nonparametric learning approach
deformable terrain
scientifically interesting sites
rough terrains
safe autonomous operation
planetary rover
terrain deformation
vehicle attitude
vehicle configuration
exteroceptive information
proprioceptive information
traversability estimation
rigid terrain assumptions
multitask Gaussian process framework
Keywords: Terrain traversability estimation, Gaussian processes, learning (artificial intelligence), planetary rovers
DOI: 10.1109/IROS.2013.6696756
ISSN: 2153-0858
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Deposited On: 06 Mar 2014 00:44
Last Modified: 08 Mar 2014 03:03

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