  We present LaLaLoc to localise in environments without the need for prior
visitation, and in a manner that is robust to large changes in scene
appearance, such as a full rearrangement of furniture. Specifically, LaLaLoc
performs localisation through latent representations of room layout. LaLaLoc
learns a rich embedding space shared between RGB panoramas and layouts inferred
from a known floor plan that encodes the structural similarity between
locations. Further, LaLaLoc introduces direct, cross-modal pose optimisation in
its latent space. Thus, LaLaLoc enables fine-grained pose estimation in a scene
without the need for prior visitation, as well as being robust to dynamics,
such as a change in furniture configuration. We show that in a domestic
environment LaLaLoc is able to accurately localise a single RGB panorama image
to within 8.3cm, given only a floor plan as a prior.
