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We present LaLaLoc to localise in environments without the need for priorvisitation, and in a manner that is robust to large changes in sceneappearance, such as a full rearrangement of furniture. Specifically, LaLaLocperforms localisation through latent representations of room layout. LaLaLoclearns a rich embedding space shared between RGB panoramas and layouts inferredfrom a known floor plan that encodes the structural similarity betweenlocations. Further, LaLaLoc introduces direct, cross-modal pose optimisation inits latent space. Thus, LaLaLoc enables fine-grained pose estimation in a scenewithout the need for prior visitation, as well as being robust to dynamics,such as a change in furniture configuration. We show that in a domesticenvironment LaLaLoc is able to accurately localise a single RGB panorama imageto within 8.3cm, given only a floor plan as a prior.

LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments