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.

LaLaLoc 利用房间布局的潜在表示进行本地化，通过潜在空间中的直接交叉模态姿态优化实现对全景图的细粒度姿态估计，从而在没有先前访问的情况下弥补了其它方法的缺陷，具有鲁棒性和较高的准确性。