3DoF Localization from a Single Image and an Object Map: the Flatlandia Problem and Dataset
This addresses the need for efficient and scalable localization for autonomous agents and augmented reality, though it is incremental as it builds on prior work using 2D maps.
The paper tackles the problem of visual localization by introducing Flatlandia, a challenge that uses 2D object maps instead of 3D models, and shows that baseline models achieve competitive accuracy compared to state-of-the-art methods.
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.