# TagSLAM: Robust SLAM with Fiducial Markers

arXiv: https://arxiv.org/abs/1910.00679 · HTML: https://mlsanity.com/paper/1910.00679/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [Event-based Continuous Color Video Decompression from Single Frames](https://mlsanity.com/paper/2312.00113/) (2023)
- [Neuromorphic Synergy for Video Binarization](https://mlsanity.com/paper/2402.12644/) (2024)
- [L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration](https://mlsanity.com/paper/2406.03298/) (2024)
- [Comparative Study of Visual SLAM-Based Mobile Robot Localization Using Fiducial Markers](https://mlsanity.com/paper/2309.04441/) (2023)
- [The Use of Multi-Scale Fiducial Markers To Aid Takeoff and Landing Navigation by Rotorcraft](https://mlsanity.com/paper/2309.08769/) (2023)
- [Instance-Agnostic Geometry and Contact Dynamics Learning](https://mlsanity.com/paper/2309.05832/) (2023)
- [The Invisible Map: Visual-Inertial SLAM with Fiducial Markers for Smartphone-based Indoor Navigation](https://mlsanity.com/paper/2310.10862/) (2023)

## Abstract (arXiv)

TagSLAM provides a convenient, flexible, and robust way of performing Simultaneous Localization and Mapping (SLAM) with AprilTag fiducial markers. By leveraging a few simple abstractions (bodies, tags, cameras), TagSLAM provides a front end to the GTSAM factor graph optimizer that makes it possible to rapidly design a range of experiments that are based on tags: full SLAM, extrinsic camera calibration with non-overlapping views, visual localization for ground truth, loop closure for odometry, pose estimation etc. We discuss in detail how TagSLAM initializes the factor graph in a robust way, and present loop closure as an application example. TagSLAM is a ROS based open source package and can be found at https://berndpfrommer.github.io/tagslam_web.