Traversability estimation for a planetary rover via experimental kernel learning in a Gaussian process framework

Ho, Ken, Peynot, Thierry, & Sukkarieh, Salah (2013) Traversability estimation for a planetary rover via experimental kernel learning in a Gaussian process framework. In Proceedings of 2013 IEEE International Conference on Robotics and Automation, IEEE, Kongresszentrum Karlsruhe, Karlsruhe, Germany, pp. 3475-3482.

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A critical requirement for safe autonomous navigation of a planetary rover is the ability to accurately estimate the traversability of the terrain. This work considers the problem of predicting the attitude and configuration angles of the platform from terrain representations that are often incomplete due to occlusions and sensor limitations. Using Gaussian Processes (GP) and exteroceptive data as training input, we can provide a continuous and complete representation of terrain traversability, with uncertainty in the output estimates. In this paper, we propose a novel method that focuses on exploiting the explicit correlation in vehicle attitude and configuration during operation by learning a kernel function from vehicle experience to perform GP regression. We provide an extensive experimental validation of the proposed method on a planetary rover. We show significant improvement in the accuracy of our estimation compared with results obtained using standard kernels (Squared Exponential and Neural Network), and compared to traversability estimation made over terrain models built using state-of-the-art GP techniques.

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6 citations in Scopus
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3 citations in Web of Science®

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ID Code: 67655
Item Type: Conference Paper
Refereed: Yes
Additional Information: 13851143
terrain traversability estimation
planetary rover
experimental kernel learning
Gaussian process framework
planetary rover autonomous navigation
attitude angle
configuration angles
exteroceptive data
terrain traversability representation
explicit correlation
vehicle attitude
vehicle configuration
kernel function learning
GP regression
squared exponential kernel
neural network kernel
Keywords: aerospace robotics, Gaussian processes, learning (artificial intelligence), neurocontrollers, path planning, planetary rovers, regression analysis
DOI: 10.1109/ICRA.2013.6631063
ISBN: 9781467356411
ISSN: 1050-4729
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 00:41
Last Modified: 07 Mar 2014 20:04

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