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This paper presents an accurate and scalable method for fiducial taglocalization on a 3D prior environmental map. The proposed method comprisesthree steps: 1) visual odometry-based landmark SLAM for estimating the relativeposes between fiducial tags, 2) geometrical matching-based global tag-mapregistration via maximum clique finding, and 3) tag pose refinement based ondirect camera-map alignment with normalized information distance. Throughsimulation-based evaluations, the proposed method achieved a 98 \% globaltag-map registration success rate and an average tag pose estimation accuracyof a few centimeters. Experimental results in a real environment demonstratedthat it enables to localize over 110 fiducial tags placed in an environment in25 minutes for data recording and post-processing.

Scalable Fiducial Tag Localization on a 3D Prior Map via Graph-Theoretic Global Tag-Map Registration