  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.
