@inproceedings {pub3345,
	title = {Embedded Robust Visual Obstacle Detection on Autonomous Lawn Mowers},
	author = {Mathias Franzius AND Mark Dunn AND Roman Dirnberger AND Nils Einecke},
	year = {2017},
	month = {July},
	abstract = {Autonomous lawn mowers have become a solid product over the past years. Yet, they lack intelligent functions like obstacle recognition and avoidance or robust mapping and localization. One reason for these missing capabilities are the challenging situations encountered outdoors. Furthermore, the intelligent functions need to be robust enough that they do not need any expert intervention whatsoever. This makes functions based on image processing particularly difficult. In this paper, we discuss problems that autonomous lawn mowers equipped with cameras will encounter and show some solutions. Additionally, we validate our approach with a large scale test where we installed several test units with cameras in 10 different countries in Europe for several months.},
	publisher = {IEEE},
	booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
	pages = {361-369}
}