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We present the design of an entire on-device system for large-scale urbanlocalization using images. The proposed design integrates compact imageretrieval and 2D-3D correspondence search to estimate the location in extensivecity regions. Our design is GPS agnostic and does not require networkconnection. In order to overcome the resource constraints of mobile devices, wepropose a system design that leverages the scalability advantage of imageretrieval and accuracy of 3D model-based localization. Furthermore, we proposea new hashing-based cascade search for fast computation of 2D-3Dcorrespondences. In addition, we propose a new one-many RANSAC for accuratepose estimation. The new one-many RANSAC addresses the challenge of repetitivebuilding structures (e.g. windows, balconies) in urban localization. Extensiveexperiments demonstrate that our 2D-3D correspondence search achievesstate-of-the-art localization accuracy on multiple benchmark datasets.Furthermore, our experiments on a large Google Street View (GSV) image datasetshow the potential of large-scale localization entirely on a typical mobiledevice.

On-device Scalable Image-based Localization via Prioritized Cascade Search and Fast One-Many RANSAC