Ground-to-Aerial Image Matching for Vehicle Localization
Z. Xia (TU Delft - Intelligent Vehicles)
J.F.P. Kooij – Promotor (TU Delft - Intelligent Vehicles)
D.M. Gavrila – Promotor (TU Delft - Intelligent Vehicles)
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Abstract
Automated driving has immense potential for improving road safety. Over the past decades, extensive research has been conducted in this field. Although the technological capability for highly automated driving exists today, its widespread application is not yet present. One major limiting factor of current automated driving solutions is that vehicle localization heavily relies on high-definition maps (HD maps), which are highly expensive to construct and maintain. This dissertation focuses on developing a more scalable solution for vehicle localization. It explores a novel technique that estimates the ego vehicle’s pose (location and orientation) by matching ground-level images captured by the vehicle’s onboard camera to publicly available geo-referenced aerial imagery...