Nonparametric traversability estimation in partially occluded and deformable terrain

Ho, Ken, Peynot, Thierry, & Sukkarieh, Salah (2016) Nonparametric traversability estimation in partially occluded and deformable terrain. Journal of Field Robotics. (In Press)

[img] Accepted Version (PDF 4MB)
Administrators only | Request a copy from author

View at publisher


Terrain traversability estimation is a fundamental requirement to ensure the safety of autonomous planetary rovers and their ability to conduct long-term missions. This paper addresses two fundamental challenges for terrain traversability estimation techniques. First, representations of terrain data, which are typically built by the rover’s onboard exteroceptive sensors, are often incomplete due to occlusions and sensor limitations. Second, during terrain traversal, the rover-terrain interaction can cause terrain deformation, which may significantly alter the difficulty of traversal. We propose a novel approach built on Gaussian process (GP) regression to learn, and consequently to predict, the rover’s attitude and chassis configuration on unstructured terrain using terrain geometry information only. First, given incomplete terrain data, we make an initial prediction under the assumption that the terrain is rigid, using a learnt kernel function. Then, we refine this initial estimate to account for the effects of potential terrain deformation, using a near-to-far learning approach based on multitask GP regression. We present an extensive experimental validation of the proposed approach on terrain that is mostly rocky and whose geometry changes as a result of loads from rover traversals. This demonstrates the ability of the proposed approach to accurately predict the rover’s attitude and configuration in partially occluded and deformable terrain.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 92561
Item Type: Journal Article
Refereed: Yes
Keywords: Planetary Rover, Machine Learning, Terrain Traversability, Gaussian Process
DOI: 10.1002/rob.21646
ISSN: 1556-4967
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
  • DIISR AUSTRALIAN GOV/Australian Space Research Program
  • AOARD/A2386-10- 1-4153
  • ACFR/.
Copyright Owner: Copyright 2016 Wiley Periodicals, Inc.
Copyright Statement: This is the peer reviewed version of the following article: Ho, K., Peynot, T. and Sukkarieh, S. (2016), Nonparametric Traversability Estimation in Partially Occluded and Deformable Terrain. J. Field Robotics. [In Press], which has been published in final form at This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Deposited On: 02 Feb 2016 00:34
Last Modified: 03 Feb 2016 02:48

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page