  Continuous maps representations, as opposed to traditional discrete ones such
as grid maps, have been gaining traction in the research community. However,
current approaches still suffer from high computation costs, making them unable
to be used in large environments without sacrificing precision. In this paper,
a scalable method building upon Gaussian Process-based Euclidean Distance
Fields (GP-EDFs) is proposed. By leveraging structure inherent to indoor
environments, namely walls and rooms, we achieve an accurate continuous map
representation that is fast enough to be updated and used in real-time. This is
possible thanks to a novel line-based room segmentation algorithm, enabling the
creation of smaller local GP-EDFs for each room, which in turn also use line
segments as its shape priors, thus representing the map more efficiently with
fewer data points. We evaluate this method in simulation experiments, and make
the code available open-source.
