  In this study, we aim to solve the single-view robot self-localization
problem by using visual experience across domains. Although the bag-of-words
method constitutes a popular approach to single-view localization, it fails
badly when it's visual vocabulary is learned and tested in different domains.
Further, we are interested in using a cross-domain setting, in which the visual
vocabulary is learned in different seasons and routes from the input
query/database scenes. Our strategy is to mine a cross-domain visual
experience, a library of raw visual images collected in different domains, to
discover the relevant visual patterns that effectively explain the input scene,
and use them for scene retrieval. In particular, we show that the appearance
and the pose of the mined visual patterns of a query scene can be efficiently
and discriminatively matched against those of the database scenes by employing
image-to-class distance and spatial pyramid matching. Experimental results
obtained using a novel cross-domain dataset show that our system achieves
promising results despite our visual vocabulary being learned and tested in
different domains.
