Computer Vision Navigation based on Fiducial Markers of Opportunity
Bibliographic record
Abstract
Visual Fiducial Markers (FM) have been extensively used for localizing in both indoor and outdoor environments. Such markers are typically comprised of known patterns and mounted in locations known to the handheld navigation device (HND). The Computer Vision (CV) algorithm applied to the camera output of the HND isolates, characterizes and identifies the FM's. In this paper, the application of the FM is extended and generalized such that arbitrary FM's of opportunity in unknown locations can be used for navigation. The FM's may consist of simple patterns known only to be a rectangle or other geometric shape of generally unknown dimensions. The other necessary but rather benign assumption is that the FM is stationary. Theoretical and experimental results will be given in this paper that will demonstrate that any known or assumed attribute of an observed FM can provide information of practical significance in the context of the navigation objective. That is, the CV processing of the observed FM's will result in constraints that can be directly applied to the navigation estimate resulting in lower uncertainty of the location of the HND. Furthermore, as will be shown, the CV processing required can be accomplished in real time with a modest processor.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".