LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments
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.
Cite
Text
Howard-Jenkins et al. "LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments." International Conference on Computer Vision, 2021. doi:10.1109/ICCV48922.2021.00995Markdown
[Howard-Jenkins et al. "LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments." International Conference on Computer Vision, 2021.](https://mlanthology.org/iccv/2021/howardjenkins2021iccv-lalaloc/) doi:10.1109/ICCV48922.2021.00995BibTeX
@inproceedings{howardjenkins2021iccv-lalaloc,
title = {{LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments}},
author = {Howard-Jenkins, Henry and Ruiz-Sarmiento, Jose-Raul and Prisacariu, Victor Adrian},
booktitle = {International Conference on Computer Vision},
year = {2021},
pages = {10107-10116},
doi = {10.1109/ICCV48922.2021.00995},
url = {https://mlanthology.org/iccv/2021/howardjenkins2021iccv-lalaloc/}
}