@inproceedings {pub2903,
	title = {Predicting the Long-term Robustness of Visual Features},
	author = {Benjamin Metka AND Annika Besetzny AND Mathias Franzius AND Ute Bauer-Wersing},
	year = {2015},
	month = {July},
	abstract = {Many vision based localization methods extract
local visual features to build a sparse map of the environment and
estimate the position of the camera from feature correspondences.
However, the majority of features is typically only detectable for
short time-frames so that most information in the map becomes
obsolete over longer periods of time. Long-term localization is
therefore a challenging problem especially in outdoor scenarios
where the appearance of the environment can change drastically
due to different day times, weather conditions or seasonal
effects. We propose to learn a model of stable and unstable
feature characteristics from texture and color information around
detected interest points that allows to predict the robustness
of visual features. The model can be incorporated into the
conventional feature extraction and matching process to reject
potentially unstable features during the mapping phase. The
application of the additional filtering step yields more compact
maps and therefore reduces the probability of false positive
matches, which can cause complete failure of a localization
system. The model is trained with recordings of a train journey on
the same track across seasons which facilitates the identification
of stable and unstable features. Experiments on data of the same
domain demonstrate the generalization capabilities of the learned
characteristics.
},
	publisher = {IEEE},
	booktitle = {International Conference on Advanced Robotics},
	city = {Istanbul}
}