Accurate Image Based Localization by Applying SFM and Coordinate System Registration
Finding accurate positions of mobile devices based on visual information involves searching for query-matching images in a very large dataset, typically containing millions of images. Although the main problem is designing a reliable image retrieval engine, accurate localization also depends on a good fusion algorithm between the GPS data (geo-tags) of each query-matching image and the query image. This paper proposes a new method for reliable estimation of the actual query camera position (geo-tag) by applying structure from motion (SFM) with bundle adjustment for sparse 3D camera position reconstruction, and a linear rigid transformation between two different 3D Cartesian coordinate systems. The experimental results on more than 170 query images show the proposed algorithm returns accurate results for a high percentage of the samples. The error range of the estimated query geo-tag is compared with other related research and indicates an average error less than 5 meters that improves on some of the published works.