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We present a novel framework using Energy-Based Models (EBMs) for localizinga ground vehicle mounted with a range sensor against satellite imagery in theabsence of GPS. Lidar sensors have become ubiquitous on autonomous vehicles fordescribing its surrounding environment. Map priors are typically built usingthe same sensor modality for localization purposes. However, these map buildingendeavors using range sensors are often expensive and time-consuming.Alternatively, we leverage the use of satellite images as map priors, which arewidely available, easily accessible, and provide comprehensive coverage. Wepropose a method using convolutional transformers that performs accuratemetric-level localization in a cross-modal manner, which is challenging due tothe drastic difference in appearance between the sparse range sensor readingsand the rich satellite imagery. We train our model end-to-end and demonstrateour approach achieving higher accuracy than the state-of-the-art on KITTI,Pandaset, and a custom dataset.

Energy-Based Models for Cross-Modal Localization using Convolutional Transformers