LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments
| Type of publication: | Inproceedings |
| Citation: | raul2021iccv |
| Booktitle: | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) |
| Year: | 2021 |
| Month: | {oct} |
| Pages: | 10107-10116 |
| URL: | http://https://openaccess.thec... |
| 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. |
| Userfields: | img_url=,rank_indexname=,rank_pos_in_category=,rank_num_in_category=,rank_cat_name=,impact_factor= |
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| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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