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In this work, we present LocGAN, our localization approach based on ageo-referenced aerial imagery and LiDAR grid maps. Currently, mostself-localization approaches relate the current sensor observations to a mapgenerated from previously acquired data. Unfortunately, this data is not alwaysavailable and the generated maps are usually sensor setup specific. GlobalNavigation Satellite Systems (GNSS) can overcome this problem. However, theyare not always reliable especially in urban areas due to multi-path andshadowing effects. Since aerial imagery is usually available, we can use it asprior information. To match aerial images with grid maps, we use conditionalGenerative Adversarial Networks (cGANs) which transform aerial images to thegrid map domain. The transformation between the predicted and measured grid mapis estimated using a localization network (LocNet). Given the geo-referencedaerial image transformation the vehicle pose can be estimated. Evaluationsperformed on the data recorded in region Karlsruhe, Germany show that ourLocGAN approach provides reliable global localization results.

Localization in Aerial Imagery with Grid Maps using LocGAN