Bibliographic record
Abstract
Autonomous robotic systems require automatic registration of data collected by on-board sensors. Techniques requiring user intervention are unsuitable for autonomous robotic applications, while iterative-based techniques do not scale well as the dataset size increases, and additionally tend towards locally minimal solutions. To avoid the latter problem, an accurate initial estimation of the transformation is required for iterative algorithms to perform properly. The method presented in this paper does not require an initial estimation of the transformation, and avoids problems of the classical iterative techniques by employing the multi-dimensional Fourier transform, which decouples the estimation of rotational parameters from the estimation of the translational parameters. Using the magnitude of the Fourier transform, an axis of rotation is estimated by determining the line that contains the minimal energy differential between two rotated 3-D images. By using a coarse to fine approach, the angle of rotation is determined from the minimal sum squared difference between the two rotated image. As the Fourier transform introduces hermitical symmetry in the rotation, the proper solution is identified through the use of a Phasecorrelation technique, and the estimate of translation is simultaneously obtained. Experimental results illustrate the accuracy that can be achieved by the proposed registration technique and performance is compared with that of the classical iterative closest point method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".