@inproceedings {PUBA289,
	title = {Outdoor self-localization of a mobile robot using SFA},
	author = {Benjamin Metka AND Mathias Franzius AND Ute Bauer-Wersing},
	year = {2013},
	month = {November},
	abstract = {We apply slow feature analysis (SFA) to the problem of selflocalization
with a mobile robot. A similar unsupervised hierarchical
model has earlier been shown to extract a virtual rat{\textquoteright}s position as slowly
varying features by directly processing the raw, high dimensional views
captured during a training run. The learned representations encode the
robot{\textquoteright}s position, are orientation invariant and similar to cells in a rodent{\textquoteright}s
hippocampus.
Here, we apply the model to virtual reality data and, for the rst time, to
data captured by a mobile outdoor robot. We extend the model by using
an omnidirectional mirror, which allows to change the perceived image
statistics for improved orientation invariance. The resulting representations
are used for the notoriously dicult task of outdoor localization
with mean absolute localization errors below 6\%.},
	publisher = {Springer },
	booktitle = {Int. Conf. on Neural Information Processing (ICONIP)},
	editor = {Minho Lee, Akira Hirose,  Zeng-Guang Hou, Rhee Man Kil},
	city = {Berlin Heidelberg},
	volume = {8226},
	number = {1},
	pages = {249-256},
	series = {Lecture Notes in Computer Science}
}