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

Kenji Koide; Shuji Oishi; Masashi Yokozuka; Atsuhiko Banno

IROS 2022poster7 citations

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

Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno

Abstract

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.

BibTeX

@inproceedings{iros2022_scalablefiducial,
  title = {Scalable Fiducial Tag Localization on a 3D Prior Map via Graph-Theoretic Global Tag-Map Registration},
  author = {Kenji Koide and Shuji Oishi and Masashi Yokozuka and Atsuhiko Banno},
  booktitle = {IROS 2022},
  year = {2022}
}