Improvements to Thin-Sheet 3D LiDAR Fiducial Tag Localization
Bibliographic record
Abstract
The intensity image-based LiDAR fiducial marker system (IFM) proposes an algorithm to localize 3D fiducials of thin-sheet tags, using patterns compatible with AprilTag and ArUco, which can be attached to other surfaces without impacting the 3D environment. Unfortunately, due to the adoption of 3D-to-2D spherical projection, IFM exhibits two limitations: 1) IFM can only detect fiducials in a single-view point cloud and does not apply to a 3D LiDAR map; and 2) as the distance between the tag and the LiDAR increases, the projection size of the tag decreases until it is too small to be detected. In this paper, aiming to tackle the limitations and benefit downstream tasks such as 3D map merging, we develop an algorithm to improve the localization of thin-sheet 3D LiDAR fiducial tags. Given a 3D point cloud, which can serve as a 3D map, with intensity information, our method automatically outputs tag poses (labeled by ID number) and vertex locations (labeled by index) with respect to the global coordinate system. In particular, we design a new pipeline that gradually analyzes the 3D point cloud of the map from the intensity and geometry perspectives, extracting potential tag-containing point clusters. Then, we introduce an intermediate-plane-based method to further check if each potential cluster has a tag and compute the vertex locations and tag pose if found. We conduct both qualitative and quantitative experiments to demonstrate that the proposed method addresses the limitations of IFM while achieving better accuracy. The open-source implementation of this work is available at:https://github.com/York-SDCNLab/Marker-Detection-General.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".