TY  - CONF
ID  - raul2021iccv
T1  - LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments
A1  - Howard-Jenkins, Henry
A1  - Ruiz-Sarmiento, J. R.
A1  - Prisacariu, Victor Adrian
TI  - Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)
Y1  - 2021
SP  - 10107
EP  - 10116
UR  - https://openaccess.thecvf.com/content/ICCV2021/papers/Howard-Jenkins_LaLaLoc_Latent_Layout_Localisation_in_Dynamic_Unvisited_Environments_ICCV_2021_paper.pdf
M2  - doi: https://doi.org/10.48550/arXiv.2104.09169
N2  - 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.
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