eprintid: 10139848
rev_number: 13
eprint_status: archive
userid: 608
dir: disk0/10/13/98/48
datestamp: 2021-12-08 10:16:48
lastmod: 2022-06-05 06:10:26
status_changed: 2021-12-08 10:16:48
type: article
metadata_visibility: show
creators_name: Mura, C
creators_name: Pajarola, R
creators_name: Schindler, K
creators_name: Mitra, N
title: Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: CCS Concepts
• Computing methodologies → Reconstruction; Object detection; Scene understanding; Machine learning;
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Recent years have seen a proliferation of new digital products for the efficient management of indoor spaces, with important applications like emergency management, virtual property showcasing and interior design. While highly innovative and effective, these products rely on accurate 3D models of the environments considered, including information on both architectural and non-permanent elements. These models must be created from measured data such as RGB-D images or 3D point clouds, whose capture and consolidation involves lengthy data workflows. This strongly limits the rate at which 3D models can be produced, preventing the adoption of many digital services for indoor space management.

We provide a radical alternative to such data-intensive procedures by presenting Walk2Map, a data-driven approach to generate floor plans only from trajectories of a person walking inside the rooms. Thanks to recent advances in data-driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer-level smartphones, which allows for an effortless and scalable mapping of real-world indoor spaces. Our work is based on learning the latent relation between an indoor walk trajectory and the information represented in a floor plan: interior space footprint, portals, and furniture. We distinguish between recovering area-related (interior footprint, furniture) and wall-related (doors) information and use two different neural architectures for the two tasks: an image-based Encoder-Decoder and a Graph Convolutional Network, respectively. We train our networks using scanned 3D indoor models and apply them in a cascaded fashion on an indoor walk trajectory at inference time.

We perform a qualitative and quantitative evaluation using both trajectories simulated from scanned models of interiors and measured, real-world trajectories, and compare against a baseline method for image-to-image translation. The experiments confirm that our technique is viable and allows recovering reliable floor plans from minimal walk trajectory data.
date: 2021-05
date_type: published
publisher: WILEY
official_url: https://doi.org/10.1111/cgf.142640
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1871878
doi: 10.1111/cgf.142640
lyricists_name: Mitra, Niloy
lyricists_id: NMITR19
actors_name: Mitra, Niloy
actors_id: NMITR19
actors_role: owner
full_text_status: public
publication: Computer Graphics Forum
volume: 40
number: 2
pagerange: 375-388
pages: 14
citation:        Mura, C;    Pajarola, R;    Schindler, K;    Mitra, N;      (2021)    Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories.                   Computer Graphics Forum , 40  (2)   pp. 375-388.    10.1111/cgf.142640 <https://doi.org/10.1111/cgf.142640>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10139848/1/walk2map.pdf