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.
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