# Comparative Study of Visual SLAM-Based Mobile Robot Localization Using Fiducial Markers

arXiv: https://arxiv.org/abs/2309.04441 · HTML: https://mlsanity.com/paper/2309.04441/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markers](https://mlsanity.com/paper/1902.03729/) (2019)
- [TagSLAM: Robust SLAM with Fiducial Markers](https://mlsanity.com/paper/1910.00679/) (2019)
- [WOLF: A modular estimation framework for robotics based on factor graphs](https://mlsanity.com/paper/2110.12919/) (2021)

## Built on by (most cited first, 1 of 1)

- [The Use of Multi-Scale Fiducial Markers To Aid Takeoff and Landing Navigation by Rotorcraft](https://mlsanity.com/paper/2309.08769/) (2023)

## Abstract (arXiv)

This paper presents a comparative study of three modes for mobile robot localization based on visual SLAM using fiducial markers (i.e., square-shaped artificial landmarks with a black-and-white grid pattern): SLAM, SLAM with a prior map, and localization with a prior map. The reason for comparing the SLAM-based approaches leveraging fiducial markers is because previous work has shown their superior performance over feature-only methods, with less computational burden compared to methods that use both feature and marker detection without compromising the localization performance. The evaluation is conducted using indoor image sequences captured with a hand-held camera containing multiple fiducial markers in the environment. The performance metrics include absolute trajectory error and runtime for the optimization process per frame. In particular, for the last two modes (SLAM and localization with a prior map), we evaluate their performances by perturbing the quality of prior map to study the extent to which each mode is tolerant to such perturbations. Hardware experiments show consistent trajectory error levels across the three modes, with the localization mode exhibiting the shortest runtime among them. Yet, with map perturbations, SLAM with a prior map maintains performance, while localization mode degrades in both aspects.