Large-scale, real-time visual–inertial localization revisited
Journal article, 2020

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 real-world 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 and storage requirements of these approaches remain open challenges. We aim to deploy localization at a 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 the appearance and geometry of the scene for efficient model storage and lookup leading to scalability beyond what has been demonstrated previously. It allows for low-latency localization queries and efficient fusion to be 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 of the world achieving query latencies in the 200 ms range.

visual tracking


sensor fusion


Simon Lynen

Google Switzerland GmbH

Swiss Federal Institute of Technology in Zürich (ETH)

Bernhard Zeisl

Google Switzerland GmbH

Dror Aiger

Google Israel

Michael Bosse

Microsoft Research

Swiss Federal Institute of Technology in Zürich (ETH)

Joel Hesch

Microsoft Research

Swiss Federal Institute of Technology in Zürich (ETH)

Marc Pollefeys

Microsoft Research

Swiss Federal Institute of Technology in Zürich (ETH)

Roland Siegwart

Swiss Federal Institute of Technology in Zürich (ETH)

Torsten Sattler

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

International Journal of Robotics Research

0278-3649 (ISSN)

Vol. 39 9 1061-1084

Subject Categories


Computer Systems

Computer Vision and Robotics (Autonomous Systems)



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