@misc {pub2900,
	title = {Visual Self-Localization in Outdoor Environments
Using Slow Feature Analysis},
	author = {Benjamin Metka AND Ute Bauer-Wersing AND Mathias Franzius},
	year = {2015},
	month = {March},
	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 first 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 difficult task of outdoor localization
with mean absolute localization errors below 6\%.},
	publisher = {AEON Verlag \& Studio GmbH \& Co. KG},
	booktitle = {Heidelberger Bildverarbeitungsforum}
}