Pose Correction for Highly Accurate Visual Localization in Large-scale Indoor Spaces

Indoor visual localization is significant for various applications such as autonomous robots, augmented reality, and mixed reality. Recent advances in visual localization have demonstrated their feasibility in large-scale indoor spaces through coarse-to-fine methods that typically employ three steps: image retrieval, pose estimation, and pose selection. However, further research is needed to improve the accuracy of large-scale indoor visual localization. We demonstrate that the limitations in the previous methods can be attributed to the sparsity of image positions in the database, which causes view-differences between a query and a retrieved image from the database. In this paper, to address this problem, we propose a novel module, named pose correction, that enables re-estimation of the pose with local feature matching in a similar view by reorganizing the local features. This module enhances the accuracy of the initially estimated pose and assigns more reliable ranks. Furthermore, the proposed method achieves a new state-of-the-art performance with an accuracy of more than 90 % within 1.0 m in the challenging indoor benchmark dataset InLoc for the first time.