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2016 | OriginalPaper | Chapter

Localization of Unmanned Aerial Vehicles Using Terrain Classification from Aerial Images

Authors : Andreas Masselli, Richard Hanten, Andreas Zell

Published in: Intelligent Autonomous Systems 13

Publisher: Springer International Publishing

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Abstract

In this paper we investigate the benefit of terrain classification for self-localization of a flying robot. The key idea is to use aerial images, which are already available from online databases such as GoogleMaps™, as reference map and to match images taken with a downward looking camera with this map. Using different terrain classes as features, we can make sure that our method is invariant to lighting/weather changes as well as seasonal variations or minor changes in the environment. A particle filter is used to register the query image with parts of the map. The proposed method has shown to work on image data from both simulated and real flights.

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Metadata
Title
Localization of Unmanned Aerial Vehicles Using Terrain Classification from Aerial Images
Authors
Andreas Masselli
Richard Hanten
Andreas Zell
Copyright Year
2016
Book
Intelligent Autonomous Systems 13

Print ISBN: 978-3-319-08337-7

Electronic ISBN: 978-3-319-08338-4

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-08338-4_60

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