  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. 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 an 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 simulated and
measured, real-world trajectories, and compare against a baseline method for
image-to-image translation. The experiments confirm the feasibility of our
approach.
