  State of the art mapping algorithms can produce high-quality maps. However,
they are still vulnerable to clutter and outliers which can affect map quality
and in consequence hinder the performance of a robot, and further map
processing for semantic understanding of the environment. This paper presents
ROSE, a method for building-level structure detection in robotic maps. ROSE
exploits the fact that indoor environments usually contain walls and
straight-line elements along a limited set of orientations. Therefore metric
maps often have a set of dominant directions. ROSE extracts these directions
and uses this information to segment the map into structure and clutter through
filtering the map in the frequency domain (an approach substantially
underutilised in the mapping applications). Removing the clutter in this way
makes wall detection (e.g. using the Hough transform) more robust. Our
experiments demonstrate that (1) the application of ROSE for decluttering can
substantially improve structural feature retrieval (e.g., walls) in cluttered
environments, (2) ROSE can successfully distinguish between clutter and
structure in the map even with substantial amount of noise and (3) ROSE can
numerically assess the amount of structure in the map.
