# Intensity Image-based LiDAR Fiducial Marker System

arXiv: https://arxiv.org/abs/2203.01816 · HTML: https://mlsanity.com/paper/2203.01816/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [GRNet: Gridding Residual Network for Dense Point Cloud Completion](https://mlsanity.com/paper/2006.03761/) (2020)
- [RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2103.10039/) (2021)
- [LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving](https://mlsanity.com/paper/1903.08701/) (2019)
- [Tightly Coupled 3D Lidar Inertial Odometry and Mapping](https://mlsanity.com/paper/1904.06993/) (2019)
- [Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV](https://mlsanity.com/paper/1909.06700/) (2019)
- [Improvements to Target-Based 3D LiDAR to Camera Calibration](https://mlsanity.com/paper/1910.03126/) (2019)
- [Good Colour Maps: How to Design Them](https://mlsanity.com/paper/1509.03700/) (2015)
- [Mapping and Localization from Planar Markers](https://mlsanity.com/paper/1606.00151/) (2016)
- [Navigation of a Self-Driving Vehicle Using One Fiducial Marker](https://mlsanity.com/paper/2104.12954/) (2021)
- [Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraints](https://mlsanity.com/paper/1909.11888/) (2019)

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

- [HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose Estimation](https://mlsanity.com/paper/2502.10606/) (2025)
- [L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration](https://mlsanity.com/paper/2406.03298/) (2024)
- [Fiducial Tag Localization on a 3D LiDAR Prior Map](https://mlsanity.com/paper/2209.01072/) (2022)
- [SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy](https://mlsanity.com/paper/2605.12247/) (2026)

## Abstract (arXiv)

The fiducial marker system for LiDAR is crucial for the robotic application but it is still rare to date. In this paper, an Intensity Image-based LiDAR Fiducial Marker (IILFM) system is developed. This system only requires an unstructured point cloud with intensity as the input and it has no restriction on marker placement and shape. A marker detection method that locates the predefined 3D fiducials in the point cloud through the intensity image is introduced. Then, an approach that utilizes the detected 3D fiducials to estimate the LiDAR 6-DOF pose that describes the transmission from the world coordinate system to the LiDAR coordinate system is developed. Moreover, all these processes run in real-time (approx 40 Hz on Livox Mid-40 and approx 143 Hz on VLP-16). Qualitative and quantitative experiments are conducted to demonstrate that the proposed system has similar convenience and accuracy as the conventional visual fiducial marker system. The codes and results are available at: https://github.com/York-SDCNLab/IILFM.