  Lifelong localization in a given map is an essential capability for
autonomous service robots. In this paper, we consider the task of long-term
localization in a changing indoor environment given sparse CAD floor plans. The
commonly used pre-built maps from the robot sensors may increase the cost and
time of deployment. Furthermore, their detailed nature requires that they are
updated when significant changes occur. We address the difficulty of
localization when the correspondence between the map and the observations is
low due to the sparsity of the CAD map and the changing environment. To
overcome both challenges, we propose to exploit semantic cues that are commonly
present in human-oriented spaces. These semantic cues can be detected using RGB
cameras by utilizing object detection, and are matched against an
easy-to-update, abstract semantic map. The semantic information is integrated
into a Monte Carlo localization framework using a particle filter that operates
on 2D LiDAR scans and camera data. We provide a long-term localization solution
and a semantic map format, for environments that undergo changes to their
interior structure and detailed geometric maps are not available. We evaluate
our localization framework on multiple challenging indoor scenarios in an
office environment, taken weeks apart. The experiments suggest that our
approach is robust to structural changes and can run on an onboard computer. We
released the open source implementation of our approach written in C++ together
with a ROS wrapper.
