  This paper presents an accurate and scalable method for fiducial tag
localization on a 3D prior environmental map. The proposed method comprises
three steps: 1) visual odometry-based landmark SLAM for estimating the relative
poses between fiducial tags, 2) geometrical matching-based global tag-map
registration via maximum clique finding, and 3) tag pose refinement based on
direct camera-map alignment with normalized information distance. Through
simulation-based evaluations, the proposed method achieved a 98 \% global
tag-map registration success rate and an average tag pose estimation accuracy
of a few centimeters. Experimental results in a real environment demonstrated
that it enables to localize over 110 fiducial tags placed in an environment in
25 minutes for data recording and post-processing.

Robotics