Lazy Data Association For Image Sequences Matching Under Substantial Appearance Changes

Olga Vysotska, Cyrill Stachniss

Abstract:

Localization is an essential capability for mobile
robots and the ability to localize in changing environments is
key to robust outdoor navigation. Robots operating over extended
periods of time should be able to handle substantial appearance
changes such as those occurring over seasons or under different
weather conditions. In this paper, we investigate the problem
of efficiently coping with seasonal appearance changes in online
localization. We propose a lazy data association approach for
matching streams of incoming images to a reference image
sequence in an online fashion. We present a search heuristic
to quickly find matches between the current image sequence
and a database using a data association graph. Our experiments
conducted under substantial seasonal changes suggest that our
approach can efficiently match image sequences while requiring
a comparably small number of image to image comparisons.