2022/02/03 by Andrea Vallone, Vallone, Andrea, Frederik Warburg +8
Computer Science · Engineering · #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics (cs.RO) #Robotics and Sensor-Based Localization #Video Surveillance and Tracking Methods #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.2202.01821
Submitted to RA-L (IROS)
arxiv created 2022/02/03 · openalex publication_date 2022/02/03 · arxiv updated 2022/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Place recognition and visual localization are particularly challenging in wide baseline configurations. In this paper, we contribute with the Danish Airs and Grounds (DAG) dataset, a large collection of street-level and aerial images targeting such cases. Its main challenge lies in the extreme viewing-angle difference between query and reference images with consequent changes in illumination and perspective. The dataset is larger and more diverse than current publicly available data, including more than 50 km of road in urban, suburban and rural areas. All images are associated with accurate 6-DoF metadata that allows the benchmarking of visual localization methods. We also propose a map-to-image re-localization pipeline, that first estimates a dense 3D reconstruction from the aerial images and then matches query street-level images to street-level renderings of the 3D model. The dataset can be downloaded at: https://frederikwarburg.github.io/DAG