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Recent years have seen a proliferation of new digital products for theefficient management of indoor spaces, with important applications likeemergency management, virtual property showcasing and interior design. Theseproducts rely on accurate 3D models of the environments considered, includinginformation on both architectural and non-permanent elements. These models mustbe created from measured data such as RGB-D images or 3D point clouds, whosecapture and consolidation involves lengthy data workflows. This strongly limitsthe rate at which 3D models can be produced, preventing the adoption of manydigital services for indoor space management. We provide an alternative to suchdata-intensive procedures by presenting Walk2Map, a data-driven approach togenerate floor plans only from trajectories of a person walking inside therooms. Thanks to recent advances in data-driven inertial odometry, suchminimalistic input data can be acquired from the IMU readings of consumer-levelsmartphones, which allows for an effortless and scalable mapping of real-worldindoor spaces. Our work is based on learning the latent relation between anindoor walk trajectory and the information represented in a floor plan:interior space footprint, portals, and furniture. We distinguish betweenrecovering area-related (interior footprint, furniture) and wall-related(doors) information and use two different neural architectures for the twotasks: an image-based Encoder-Decoder and a Graph Convolutional Network,respectively. We train our networks using scanned 3D indoor models and applythem in a cascaded fashion on an indoor walk trajectory at inference time. Weperform a qualitative and quantitative evaluation using both simulated andmeasured, real-world trajectories, and compare against a baseline method forimage-to-image translation. The experiments confirm the feasibility of ourapproach.

Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories