  Robust localization in a given map is a crucial component of most autonomous
robots. In this paper, we address the problem of localizing in an indoor
environment that changes and where prominent structures have no correspondence
in the map built at a different point in time. To overcome the discrepancy
between the map and the observed environment caused by such changes, we exploit
human-readable localization cues to assist localization. These cues are readily
available in most facilities and can be detected using RGB camera images by
utilizing text spotting. We integrate these cues into a Monte Carlo
localization framework using a particle filter that operates on 2D LiDAR scans
and camera data. By this, we provide a robust localization solution for
environments with structural changes and dynamics by humans walking. We
evaluate our localization framework on multiple challenging indoor scenarios in
an office environment. The experiments suggest that our approach is robust to
structural changes and can run on an onboard computer. We release an open
source implementation of our approach (upon paper acceptance), which uses
off-the-shelf text spotting, written in C++ with a ROS wrapper.
