Scale Drift Correction of Camera Geo-Localization using Geo-Tagged Images
This addresses scale drift for video analysis and autonomous navigation, but is incremental as it builds on existing SfM and optimization techniques.
The paper tackles the problem of scale drift in monocular camera geo-localization by proposing a framework that integrates incremental structure from motion with scale drift correction using geo-tagged images, achieving accurate geo-localization in kilometer-scale environments.
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.