Global Aerial Localisation Using Image and Map Embeddings

Obed N Samano Abonce*, Mengjie Zhou, Andrew Calway

*Corresponding author for this work

Research output: Contribution to conferenceConference Paperpeer-review


We present a purely vision based geolocation method for aircraft flying over urban and suburban environments. The method is based on matching aerial images with geolocated map tiles using a shared low dimensional embedded space of descriptors. The Euclidean distance between descriptors is used as a similarity measure between domains. The similarity between the observation and map locations is then integrated with visual odometry to track the aircraft's position and yaw using a particle filter. Furthermore, we propose an efficient method to generate map descriptors in testing time based on interpolation, allowing compact representation of large areas giving the potential for high levels of scalability. We experimented in different cities with areas above 20 km^2 in size and preliminary results based on a database of aerial imagery demonstrate that the method gives good results.
Original languageEnglish
Publication statusAccepted/In press - 2021
Event2021 IEEE International Conference on Robotics and Automation (ICRA) - Xi'an, China
Duration: 30 May 20215 Jun 2021


Conference2021 IEEE International Conference on Robotics and Automation (ICRA)


  • Localisation, aerial images, maps


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