Monocular Urban Localization using Street View

TL;DR

This paper introduces a novel method for precise urban localization using only a monocular camera and Google Street View, leveraging topological recognition and bundle adjustment to achieve sub-meter accuracy.

Contribution

It is the first work to demonstrate global urban localization solely with a single camera and Street View data, combining topological and metric approaches.

Findings

Achieves sub-meter localization accuracy in urban environments.

Demonstrates robustness to viewpoint changes, illumination, and occlusion.

Validates method on a 3 km urban test area.

Abstract

This paper presents a metric global localization in the urban environment only with a monocular camera and the Google Street View database. We fully leverage the abundant sources from the Street View and benefits from its topo-metric structure to build a coarse-to-fine positioning, namely a topological place recognition process and then a metric pose estimation by local bundle adjustment. Our method is tested on a 3 km urban environment and demonstrates both sub-meter accuracy and robustness to viewpoint changes, illumination and occlusion. To our knowledge, this is the first work that studies the global urban localization simply with a single camera and Street View.