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

arXiv: https://arxiv.org/abs/2207.11942 · HTML: https://mlsanity.com/paper/2207.11942/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2022.

## Built on by (most cited first, 3 of 3)

- [CLIP-Clique: Graph-based Correspondence Matching Augmented by Vision Language Models for Object-based Global Localization](https://mlsanity.com/paper/2410.03054/) (2024)
- [Fiducial Tag Localization on a 3D LiDAR Prior Map](https://mlsanity.com/paper/2209.01072/) (2022)
- [Single-Shot Global Localization via Graph-Theoretic Correspondence Matching](https://mlsanity.com/paper/2306.03641/) (2023)

## Abstract (arXiv)

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.