Robust Large-Scale Localization in 3D Point Clouds Revisited
This work addresses localization in 3D environments, but it is incremental as it builds on prior methods without introducing a fundamentally new approach.
The paper re-evaluates existing algorithms for 6-DOF pose estimation from images in 3D point clouds, proposing improvements to point selection, RANSAC, and inlier tests, but does not report specific numerical results.
We tackle the problem of getting a full 6-DOF pose estimation of a query image inside a given point cloud. This technical report re-evaluates the algorithms proposed by Y. Li et al. "Worldwide Pose Estimation using 3D Point Cloud". Our code computes poses from 3 or 4 points, with both known and unknown focal length. The results can easily be displayed and analyzed with Meshlab. We found both advantages and shortcomings of the methods proposed. Furthermore, additional priors and parameters for point selection, RANSAC and pose quality estimate (inlier test) are proposed and applied.