  How to divide floor plans or navigation maps into semantic representations,
such as rooms and corridors, is an important research question in fields such
as human-robot interaction, place categorization, or semantic mapping. While
most works focus on segmenting robot built maps, those are not the only types
of map a robot, or its user, can use. We present a method for segmenting maps
from different modalities, focusing on robot built maps and hand-drawn sketch
maps, and show better results than state of the art for both types. Our method
segments the map by doing a convolution between the distance image of the map
and a circular kernel, and grouping pixels of the same value. Segmentation is
done by detecting ripple-like patterns where pixel values varies quickly, and
merging neighboring regions with similar values. We identify a flaw in the
segmentation evaluation metric used in recent works and propose a metric based
on Matthews correlation coefficient (MCC). We compare our results to
ground-truth segmentations of maps from a publicly available dataset, on which
we obtain a better MCC than the state of the art with 0.98 compared to 0.65 for
a recent Voronoi-based segmentation method and 0.70 for the DuDe segmentation
method. We also provide a dataset of sketches of an indoor environment, with
two possible sets of ground truth segmentations, on which our method obtains an
MCC of 0.56 against 0.28 for the Voronoi-based segmentation method and 0.30 for
DuDe.
