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

TL;DR

This paper introduces an on-device large-scale urban localization system that combines efficient image retrieval, a novel cascade search, and a one-many RANSAC for accurate and scalable pose estimation without network reliance.

Contribution

It presents a new hashing-based cascade search and a one-many RANSAC method, enabling fast and accurate localization on resource-constrained mobile devices.

Findings

Achieves state-of-the-art accuracy on multiple benchmarks.

Demonstrates effective large-scale localization on a mobile device.

Shows potential for GPS-independent urban localization.

Abstract

We present the design of an entire on-device system for large-scale urban localization using images. The proposed design integrates compact image retrieval and 2D-3D correspondence search to estimate the location in extensive city regions. Our design is GPS agnostic and does not require network connection. In order to overcome the resource constraints of mobile devices, we propose a system design that leverages the scalability advantage of image retrieval and accuracy of 3D model-based localization. Furthermore, we propose a new hashing-based cascade search for fast computation of 2D-3D correspondences. In addition, we propose a new one-many RANSAC for accurate pose estimation. The new one-many RANSAC addresses the challenge of repetitive building structures (e.g. windows, balconies) in urban localization. Extensive experiments demonstrate that our 2D-3D correspondence search achieves state-of-the-art localization accuracy on multiple benchmark datasets. Furthermore, our experiments on a large Google Street View (GSV) image dataset show the potential of large-scale localization entirely on a typical mobile device.