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@INPROCEEDINGS{raul2021iccv,
     author = {Howard-Jenkins, Henry and Ruiz-Sarmiento, J. R. and Prisacariu, Victor Adrian},
      month = {{oct}},
      title = {LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
       year = {2021},
        url = {https://openaccess.thecvf.com/content/ICCV2021/papers/Howard-Jenkins_LaLaLoc_Latent_Layout_Localisation_in_Dynamic_Unvisited_Environments_ICCV_2021_paper.pdf},
        doi = {https://doi.org/10.48550/arXiv.2104.09169},
   abstract = {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.},
      pages = {10107--10116}
}