  The overarching goals in image-based localization are scale, robustness and
speed. In recent years, approaches based on local features and sparse 3D
point-cloud models have both dominated the benchmarks and seen successful
realworld deployment. They enable applications ranging from robot navigation,
autonomous driving, virtual and augmented reality to device geo-localization.
Recently end-to-end learned localization approaches have been proposed which
show promising results on small scale datasets. However the positioning
accuracy, scalability, latency and compute & storage requirements of these
approaches remain open challenges. We aim to deploy localization at
global-scale where one thus relies on methods using local features and sparse
3D models. Our approach spans from offline model building to real-time
client-side pose fusion. The system compresses appearance and geometry of the
scene for efficient model storage and lookup leading to scalability beyond what
what has been previously demonstrated. It allows for low-latency localization
queries and efficient fusion run in real-time on mobile platforms by combining
server-side localization with real-time visual-inertial-based camera pose
tracking. In order to further improve efficiency we leverage a combination of
priors, nearest neighbor search, geometric match culling and a cascaded pose
candidate refinement step. This combination outperforms previous approaches
when working with large scale models and allows deployment at unprecedented
scale. We demonstrate the effectiveness of our approach on a proof-of-concept
system localizing 2.5 million images against models from four cities in
different regions on the world achieving query latencies in the 200ms range.
