@inproceedings {pub3895,
	title = {Robust Outdoor Self-localization In Changing Environments},
	author = {Muhammad  Haris AND Mathias Franzius AND Ute Bauer-Wersing},
	year = {2019},
	abstract = {In outdoor scenarios changing conditions (e.g.,
seasonal, weather and lighting effects) have a substantial impact
on the appearance of a scene, which often prevents successful
visual localization. The application of an unsupervised Slow
Feature Analysis (SFA) on the images captured by an autonomous robot enables self-localization from a single image.
However, changes occurring during the training phase or over a more extended period can affect the learned representations. To
address the problem, we propose to join long-term recordings
from an outdoor environment based on their position correspondences. The established hierarchical model trained on raw images performs well, but as an extension, we extract Fourier
components of the views and use that for learning of spatial
representations, which reduces the computation time and makes
it adequate to run on an ARM embedded system. We present
the experimental results from a simulated environment and
real-world outdoor recordings collected over a full year, which
has effects like different day time, weather, seasons and dynamic
objects. Results show an increasing invariance w.r.t. changing
conditions over time, thus an outdoor robot can improve its
localization performance during operation.},
	publisher = {IEEE},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)}
}