We present a novel approach to geolocalising panoramic images on a 2-D cartographic map based on learning a low dimensional embedded space, which allows a comparison between an image captured at a location and local neighbourhoods of the map. The representation is not sufficiently discriminatory to allow localisation from a single image, but when concatenated along a route, localisation converges quickly, with over 90% accuracy being achieved for routes of around 200m in length when using Google Street View and Open Street Map data. The method generalises a previous fixed semantic feature based approach and achieves significantly higher localisation accuracy and faster convergence.
@article{arxiv.1911.08797,
title = {You Are Here: Geolocation by Embedding Maps and Images},
author = {Noe Samano and Mengjie Zhou and Andrew Calway},
journal= {arXiv preprint arXiv:1911.08797},
year = {2020}
}
Comments
18 pages, new version accepted for ECCV 2020 (poster), with new results on publicly available dataset and comparison with implementation of previously published alternative approach