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Efficient Large-Scale Localization by Global Instance Recognition

Hierarchical frameworks consisting of both coarse and fine localization are often used as the standard pipeline for large-scale visual localization. Despite their promising performance in simple environments, they still suffer from low efficiency and accuracy in large-scale scenes, especially under challenging conditions. In this paper, we propose an efficient and accurate large-scale localization framework based on the recognition of buildings, which are not only discriminative for coarse localization but also robust for fine localization. Specifically, we assign each building instance a global ID and perform pixel-wise recognition of these global instances in the localization process. For coarse localization, we employ an efficient reference search strategy to find candidates progressively from the local map observing recognized instances instead of the whole database. For fine localization, predicted labels are further used for instance-wise feature detection and matching, allowing our model to focus on fewer but more robust keypoints for establishing correspondences. The experiments in long-term large-scale localization datasets including Aachen and RobotCar-Seasons demonstrate that our method outperforms previous approaches consistently in terms of both efficiency and accuracy.

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