Bibliographic record
Abstract
The Normal Distributions Transform (NDT) scan registration algorithm divides a point cloud using rectilinear voxel cells, then models the points within each cell as a set of Gaussian distributions. A nonlinear optimization is performed in order to register the scans, however the voxel-based approach results in ill-defined cost function derivatives as points cross cell boundaries. In this work, a Segmented Region Growing NDT (SRG-NDT) variant is proposed, which first removes the ground points from the scan, then uses natural features in the environment to generate Gaussian clusters for the NDT algorithm. The removal of the ground points is shown to significantly speed up the scan registration process with negligible effect on the registration accuracy. By clustering the remaining points, the SRG-NDT approach is able to model the environment with fewer Gaussian distributions compared with the voxel-based NDT methods, which allows for a smooth and continuous cost function that guarantees that the optimization will converge. Furthermore, the use of a relatively small number of Gaussian distributions allows for a significant improvement in run-time. Experiments in both urban and forested environments demonstrate that the SRG-NDT approach is able to achieve comparable accuracy to existing methods, but with an average decrease in computation time over ICP, G-ICP, and NDT, of 90.1%, 95.3%, and 94.5%, respectively.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".