Indoor Visual Localization using Point and Line Correspondences in dense colored point cloud

We propose a novel pipeline called Loc-PL that uses both points and lines for indoor visual localization in dense colored point cloud. Loc-PL utilizes the spatially complementary relationship between points and lines to address challenging indoor issues. There are two successive camera pose estimation modules. The first improves robustness against repetitive patterns by considering the geometric consistency of points and lines. The second utilizes points and lines to refine poses by Perspective-m-Point-n-Line (PmPnL) and circumvents unstable localization due to locally concentrated matches caused by less-textured environments. The modules use different schemes to obtain line correspondences; the first finds line matches using RANSAC, which is effective for image pairs with large viewpoint gaps, and the second utilizes rendered images from dense point cloud to get them by feature line matching. In addition, we develop a simple but effective module for evaluating the correctness of camera poses using matched point distances across two images. The experimental results on a large dataset, InLoc, show that Loc-PL achieves the state-of-the-art in four out of six scores.