@article {pub3142,
	title = {Bio-inspired visual self-localization in real world scenarios
using Slow Feature Analysis},
	author = {Benjamin Metka AND Ute Bauer-Wersing AND Mathias Franzius},
	year = {2018},
	abstract = {We present a biologically motivated model for visual self-localization which extracts a
spatial representation of the environment directly from high dimensional image data
by employing a single unsupervised learning rule. The resulting representation encodes
the position of the camera as slowly varying features while being invariant to its
orientation resembling place cells in a rodent{\textquoteright}s hippocampus. Using an
omnidirectional mirror allows to manipulate the image statistics by adding simulated
rotational movement for improved orientation invariance. We apply the
straightforward model in indoor and outdoor experiments and compare its
performance against two state of the art visual SLAM methods achieving a
competitive localization performance. Resulting localization accuracies of the
proposed model are between 0.9\% and 1.7\% with respect to the travelled distance.},
	publisher = {PLoS One},
	journal = {PLOS ONE},
	editor = {Joerg Heber}
}