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

TL;DR

This paper introduces a scalable, accurate method for localizing fiducial tags on a 3D map using graph-theoretic registration, achieving high success rates and precise pose estimation in real environments.

Contribution

It presents a novel three-step approach combining visual odometry, geometric matching, and pose refinement for fiducial tag localization on 3D maps.

Findings

98% success rate in global registration

Tag pose accuracy of a few centimeters

Localization of over 110 tags in 25 minutes

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.