  How does a person work out their location using a floorplan? It is probably
safe to say that we do not explicitly measure depths to every visible surface
and try to match them against different pose estimates in the floorplan. And
yet, this is exactly how most robotic scan-matching algorithms operate.
Similarly, we do not extrude the 2D geometry present in the floorplan into 3D
and try to align it to the real-world. And yet, this is how most vision-based
approaches localise.
  Humans do the exact opposite. Instead of depth, we use high level semantic
cues. Instead of extruding the floorplan up into the third dimension, we
collapse the 3D world into a 2D representation. Evidence of this is that many
of the floorplans we use in everyday life are not accurate, opting instead for
high levels of discriminative landmarks.
  In this work, we use this insight to present a global localisation approach
that relies solely on the semantic labels present in the floorplan and
extracted from RGB images. While our approach is able to use range measurements
if available, we demonstrate that they are unnecessary as we can achieve
results comparable to state-of-the-art without them.
