Local Quaternion Weighted Difference Functions for Orientation Calibration on Electromagnetic Trackers
Bibliographic record
Abstract
The accuracy of the electromagnetic tracking systems has been always an important issue with application to motion and kinematic analysis. Applications in virtual reality and gesture recognition require not only of improved accuracy but also fast error compensation. Several analytic methods have been used in order to correct the position error and they are well known and fast: polynomial fitting, calibration tables, and more recent, neural networks. We are interested in the orientation calibration of working spaces with possible high distortion conditions. Such conditions are prevalent in virtual environment spaces such as the CAVE and it is not always possible to avoid metallic components in the surroundings. We introduce a calibration method for a multiple-sensor electromagnetic tracking system in an environment with highly electromagnetic distortional conditions. The target system is a twelve-sensor Ultratrak Polhemus Inc./sup /spl trade// system. We compare two possible formulations: global parameter estimation and local parameter estimation for the corrective functions. It is assumed that the inverse quaternion error Q/sup -1//spl isin/ exists and it is a function of the three-dimensional location: Q/sup -1//spl isin/ /spl rarr/ f(x,y,z).
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".