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.

该研究论文使用增量 SfM 和基于地理标记图像的尺度漂移校正方法来解决单眼视频中的尺度漂移问题，并在公共数据集上进行实验验证，成果表现出了在千米尺度环境下实现了准确的地理定位