In this paper we investigate the effectiveness of SURF features for visual terrain classification for outdoor flying robots. A quadrocopter fitted with a single camera is flown over different terrains to take images of the ground below. Each image is divided into a grid and SURF features are calculated at grid intersections. A classifier is then used to learn to differentiate between different terrain types. Classification results of the SURF descriptor are compared with results from other texture descriptors like Local Binary Patterns and Local Ternary Patterns. Six different terrain types are considered in this approcah. Random forests are used for classification on each descriptor. It is shown that SURF features perform better than other descriptors at higher resolutions.
@inproceedings{khan12,
author = {Yasir Niaz Khan and Andreas Masselli and Andreas Zell},
title = {Visual Terrain Classification by Flying Robots},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
year = {2012},
pages = {498 -503},
address = {St. Paul, Minnesota, USA},
month = {may},
abstract = {In this paper we investigate the effectiveness of SURF features for
visual terrain classification for outdoor flying robots. A quadrocopter
fitted with a single camera is flown over different terrains to take
images of the ground below. Each image is divided into a grid and
SURF features are calculated at grid intersections. A classifier
is then used to learn to differentiate between different terrain
types. Classification results of the SURF descriptor are compared
with results from other texture descriptors like Local Binary Patterns
and Local Ternary Patterns. Six different terrain types are considered
in this approcah. Random forests are used for classification on each
descriptor. It is shown that SURF features perform better than other
descriptors at higher resolutions.},
doi = {10.1109/ICRA.2012.6224988},
issn = {1050-4729},
url = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2012/khan2012icra.pdf}
}