@inproceedings {pub3159,
	title = {Improving Robustness of Slow Feature Analysis Based Localization Using Loop Closure Events},
	author = {Benjamin Metka AND Ute Bauer-Wersing AND Mathias Franzius},
	year = {2016},
	abstract = {Hierarchical Slow Feature Analysis (SFA) extracts a spatial
representation of the environment by directly processing images from a
training run and has been shown to enable self-localization of a mobile
robot by encoding its position as slowly varying features. However, in
real world outdoor scenarios other variables, like global illumination or
location of dynamic objects, might vary on an equal or slower time scale
than the position of the robot. To prevent encoding of said variables we
propose to restructure the temporal order of training samples based on
loop closures in the trajectory. Every time the robot passes by a previously
visited place, former recorded images are re-inserted to increase
temporal variation of environmental variables. Hence, it is a feedback signal
enforcing the model to produce similar outputs due to its slowness
objective. Experiments in a simulated outdoor environment demonstrate
increased robustness especially for changing lighting conditions.},
	publisher = {Springer},
	booktitle = {International Conference on Artificial Neural Networks (ICANN)},
	city = {Barcelona}
}