You Are Here: Geolocation by Embedding Maps and Images

Abstract

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.

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

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• Adam: A Method for Stochastic Optimization
• In Defense of the Triplet Loss for Person Re-Identification
• Variational End-to-End Navigation and Localization
• The StreetLearn Environment and Dataset
• Learning from Maps: Visual Common Sense for Autonomous Driving
• Semantic Image Based Geolocation Given a Map
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Cited by in corpus (1)

• Leveraging an Efficient and Semantic Location Embedding to Seek New Ports of Bike Share Services