Humans can orient themselves in their 3D environments using simple 2D maps. Differently,
algorithms for visual localization mostly rely on complex 3D point clouds that are expensive …

Image retrieval-based cross-view localization methods often lead to very coarse camera
pose estimation, due to the limited sampling density of the database satellite images. In this …

This paper proposes a novel method for vision-based metric cross-view geolocalization
(CVGL) that matches the camera images captured from a ground-based vehicle with an …

This work addresses cross-view camera pose estimation, ie, determining the 3-Degrees-of-
Freedom camera pose of a given ground-level image wrt an aerial image of the local area …

In this paper, we introduce a novel approach to fine-grained cross-view geo-localization. Our
method aligns a warped ground image with a corresponding GPS-tagged satellite image …

Semantic 2D maps are commonly used by humans and machines for navigation purposes,
whether it's walking or driving. However, these maps have limitations: they lack detail, often …

This paper proposes a fine-grained self-localization method for outdoor robotics that utilizes
a flexible number of onboard cameras and readily accessible satellite images. The …

We propose a novel end-to-end method for cross-view pose estimation. Given a ground-
level query image and an aerial image that covers the query's local neighborhood, the 3 …

Efficient relocalization is essential for intelligent vehicles when GPS reception is insufficient
or sensor-based localization fails. Recent advances in Bird's-Eye-View (BEV) segmentation …

In this paper we propose an efficient data-driven solution to self-localization within a
floorplan. Floorplan data is readily available long-term persistent and inherently robust to …