Planning most-likely paths from overhead imagery

Murphy, Liz & Newman, Paul (2010) Planning most-likely paths from overhead imagery. In Kumar, V (Ed.) 2010 IEEE International Conference on Robotics and Automation, IEEE (Institute of Electrical and Electronics Engineers), Anchorage, AK, pp. 3059-3064.

[img] Published Version (PDF 1MB)
Administrators only | Request a copy from author

View at publisher


This paper is about planning paths from overhead imagery, the novelty of which is taking explicit account of uncertainty in terrain classification and spatial variation in terrain cost. The image is first classified using a multi-class Gaussian Process Classifier which provides probabilities of class membership at each location in the image. The probability of class membership at a particular grid location is then combined with a terrain cost evaluated at that location using a spatial Gaussian process. The resulting cost function is, in turn, passed to a planner. This allows both the uncertainty in terrain classification and spatial variations in terrain costs to be incorporated into the planned path. Because the cost of traversing a grid cell is now a probability density rather than a single scalar value, we can produce not only the most-likely shortest path between points on the map, but also sample from the cost map to produce a distribution of paths between the points. Results are shown in the form of planned paths over aerial maps, these paths are shown to vary in response to local variations in terrain cost.

Impact and interest:

12 citations in Scopus
4 citations in Web of Science®
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: 48507
Item Type: Conference Paper
Refereed: Yes
DOI: 10.1109/ROBOT.2010.5509501
ISBN: 978-1-4244-5038-1
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 Statement: ©2010 IEEE Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 copyright component of this work in other works.
Deposited On: 08 Feb 2012 00:52
Last Modified: 11 Mar 2012 05:49

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page