Analysing the Impact of Learning Inputs - Application to Terrain Traversability Estimation

Ho, Ken, Peynot, Thierry, & Sukkarieh, Salah (2015) Analysing the Impact of Learning Inputs - Application to Terrain Traversability Estimation. In ARAA Australasian Conference on Robotics and Automation, ARAA, Canberra, ACT, Australia.

View at publisher (open access)


Data-driven approaches such as Gaussian Process (GP) regression have been used extensively in recent robotics literature to achieve estimation by learning from experience. To ensure satisfactory performance, in most cases, multiple learning inputs are required. Intuitively, adding new inputs can often contribute to better estimation accuracy, however, it may come at the cost of a new sensor, larger training dataset and/or more complex learning, some- times for limited benefits. Therefore, it is crucial to have a systematic procedure to determine the actual impact each input has on the estimation performance. To address this issue, in this paper we propose to analyse the impact of each input on the estimate using a variance-based sensitivity analysis method. We propose an approach built on Analysis of Variance (ANOVA) decomposition, which can characterise how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We apply the proposed approach to a terrain-traversability estimation method we proposed in prior work, which is based on multi-task GP regression, and we validate this implementation experimentally using a rover on a Mars-analogue terrain.

Impact and interest:

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.

Full-text downloads:

15 since deposited on 02 Feb 2016
8 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 92570
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Mobile robotics, Terrain Traversability Estimation, Machine Learning, Gaussian Process
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Please consult the author
Deposited On: 02 Feb 2016 05:49
Last Modified: 25 Jun 2017 13:49

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page