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Topological map learning from outdoor image sequences

Topological map learning from outdoor image sequences We propose an approach to building topological maps of environments based on image sequences. The central idea is to use manifold constraints to find representative feature prototypes, so that images can be related to each other, and thereby to camera poses in the environment. Our topological map is built incrementally, performing well after only a few visits to a location. We compare our method to several other approaches to representing images. During tests on novel images from the same environment, our method attains the highest accuracy in finding images depicting similar camera poses, including generalizing across considerable seasonal variations. © 2007 Wiley Periodicals, Inc. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Field Robotics Wiley

Topological map learning from outdoor image sequences

Journal of Field Robotics , Volume 23 (11‐12) – Nov 1, 2006

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References (27)

Publisher
Wiley
Copyright
Copyright © 2007 Wiley Periodicals, Inc., A Wiley Company
ISSN
1556-4959
eISSN
1556-4967
DOI
10.1002/rob.20170
Publisher site
See Article on Publisher Site

Abstract

We propose an approach to building topological maps of environments based on image sequences. The central idea is to use manifold constraints to find representative feature prototypes, so that images can be related to each other, and thereby to camera poses in the environment. Our topological map is built incrementally, performing well after only a few visits to a location. We compare our method to several other approaches to representing images. During tests on novel images from the same environment, our method attains the highest accuracy in finding images depicting similar camera poses, including generalizing across considerable seasonal variations. © 2007 Wiley Periodicals, Inc.

Journal

Journal of Field RoboticsWiley

Published: Nov 1, 2006

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