You are here! Finding position and orientation on a 2D map from a single image: The Flatlandia localization problem and dataset

Abstract

We introduce Flatlandia, a novel problem for visual localization of an image from object detections composed of two specific tasks: i) Coarse Map Localization: localizing a single image observing a set of objects in respect to a 2D map of object landmarks; ii) Fine-grained 3DoF Localization: estimating latitude, longitude, and orientation of the image within a 2D map. Solutions for these new tasks exploit the wide availability of open urban maps annotated with GPS locations of common objects (\eg via surveying or crowd-sourced). Such maps are also more storage-friendly than standard large-scale 3D models often used in visual localization while additionally being privacy-preserving. As existing datasets are unsuited for the proposed problem, we provide the Flatlandia dataset, designed for 3DoF visual localization in multiple urban settings and based on crowd-sourced data from five European cities. We use the Flatlandia dataset to validate the complexity of the proposed tasks