# Event-Based high-speed low-latency fiducial marker tracking

arXiv: https://arxiv.org/abs/2110.05819 · HTML: https://mlsanity.com/paper/2110.05819/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [BOP Challenge 2020 on 6D Object Localization](https://mlsanity.com/paper/2009.07378/) (2020)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy](https://mlsanity.com/paper/2009.08283/) (2020)
- [RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking](https://mlsanity.com/paper/2006.05011/) (2020)
- [DeepTag: A General Framework for Fiducial Marker Design and Detection](https://mlsanity.com/paper/2105.13731/) (2021)
- [STag: A Stable Fiducial Marker System](https://mlsanity.com/paper/1707.06292/) (2017)
- [TopoTag: A Robust and Scalable Topological Fiducial Marker System](https://mlsanity.com/paper/1908.01450/) (2019)
- [Detection of Binary Square Fiducial Markers Using an Event Camera](https://mlsanity.com/paper/2012.06516/) (2020)
- [AprilTags 3D: Dynamic Fiducial Markers for Robust Pose Estimation in Highly Reflective Environments and Indirect Communication in Swarm Robotics](https://mlsanity.com/paper/2001.08622/) (2020)

## Built on by (most cited first, 4 of 4)

- [A Monocular Event-Camera Motion Capture System](https://mlsanity.com/paper/2502.12113/) (2025)
- [Neuromorphic Synergy for Video Binarization](https://mlsanity.com/paper/2402.12644/) (2024)
- [Event6D: Event-based Novel Object 6D Pose Tracking](https://mlsanity.com/paper/2603.28045/) (2026)
- [Event-based Optical Marker Systems: A survey](https://mlsanity.com/paper/2504.20736/) (2025)

## Abstract (arXiv)

Motion and dynamic environments, especially under challenging lighting conditions, are still an open issue for robust robotic applications. In this paper, we propose an end-to-end pipeline for real-time, low latency, 6 degrees-of-freedom pose estimation of fiducial markers. Instead of achieving a pose estimation through a conventional frame-based approach, we employ the high-speed abilities of event-based sensors to directly refine the spatial transformation, using consecutive events. Furthermore, we introduce a novel two-way verification process for detecting tracking errors by backtracking the estimated pose, allowing us to evaluate the quality of our tracking. This approach allows us to achieve pose estimation at a rate up to 156~kHz, while only relying on CPU resources. The average end-to-end latency of our method is 3~ms. Experimental results demonstrate outstanding potential for robotic tasks, such as visual servoing in fast action-perception loops.