Scale Drift Correction of Camera Geo-Localization using Geo-Tagged Images

TL;DR

This paper introduces a framework that combines structure from motion and geo-tagged images to correct scale drift in monocular camera geo-localization, enabling accurate large-scale localization.

Contribution

It presents a novel method for correcting scale drift by integrating geo-tagged images with pose graph optimization and bundle adjustment in large environments.

Findings

Successfully corrects scale drift in large-scale datasets

Achieves accurate geo-localization over kilometer-scale environments

Outperforms existing methods in scale correction

Abstract

Camera geo-localization from a monocular video is a fundamental task for video analysis and autonomous navigation. Although 3D reconstruction is a key technique to obtain camera poses, monocular 3D reconstruction in a large environment tends to result in the accumulation of errors in rotation, translation, and especially in scale: a problem known as scale drift. To overcome these errors, we propose a novel framework that integrates incremental structure from motion (SfM) and a scale drift correction method utilizing geo-tagged images, such as those provided by Google Street View. Our correction method begins by obtaining sparse 6-DoF correspondences between the reconstructed 3D map coordinate system and the world coordinate system, by using geo-tagged images. Then, it corrects scale drift by applying pose graph optimization over Sim(3) constraints and bundle adjustment. Experimental evaluations on large-scale datasets show that the proposed framework not only sufficiently corrects scale drift, but also achieves accurate geo-localization in a kilometer-scale environment.