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Planning Most-Likely Paths From Overhead Imagery

Abstract:

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 Gaussia...

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Publication status:
Published

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Publisher copy:
10.1109/ROBOT.2010.5509501

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Host title:
2010 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
Pages:
3059-3064
Publication date:
2010-01-01
DOI:
ISSN:
1050-4729
ISBN:
9781424450381
Pubs id:
pubs:107116
UUID:
uuid:6907fd87-d1eb-491e-8d48-17ceb128040c
Local pid:
pubs:107116
Source identifiers:
107116
Deposit date:
2012-12-19

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