  Efficient visual localization is crucial to many applications, such as
large-scale deployment of autonomous agents and augmented reality. Traditional
visual localization, while achieving remarkable accuracy, relies on extensive
3D models of the scene or large collections of geolocalized images, which are
often inefficient to store and to scale to novel environments. In contrast,
humans orient themselves using very abstract 2D maps, using the location of
clearly identifiable landmarks. Drawing on this and on the success of recent
works that explored localization on 2D abstract maps, we propose Flatlandia, a
novel visual localization challenge. With Flatlandia, we investigate whether it
is possible to localize a visual query by comparing the layout of its common
objects detected against the known spatial layout of objects in the map. We
formalize the challenge as two tasks at different levels of accuracy to
investigate the problem and its possible limitations; for each, we propose
initial baseline models and compare them against state-of-the-art 6DoF and 3DoF
methods. Code and dataset are publicly available at
github.com/IIT-PAVIS/Flatlandia.
