Localization of Unmanned Aerial Vehicles Using Terrain Classification from Aerial Images

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 TM , 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.

BibTeX

@inproceedings{Masselli2014IAS,
  author = {Andreas Masselli and Richard Hanten and Andreas Zell},
  title = {Localization of Unmanned Aerial Vehicles Using Terrain Classification
	from Aerial Images},
  booktitle = {2014 International Conference on Intelligent Autonomous Systems (IAS-13)},
  year = {2014},
  address = {Padova, Italy},
  month = {July},
  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 TM , 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.},
}