Registration of range measurements with compact surface representation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper introduces an automatic approach for the estimation of registration parameters between successive viewpoints visited by a laser range sensor. The proposed technique works directly on the raw range measurements and does not require any external device for pose estimation nor any sophisticated feature extraction or triangular mesh computation. Assuming only object rigidity and some overlap between the scanned areas, the approach allows to estimate the full set of six parameters that define geometrical transformations in three-dimensional space. A compact modified Gauss sphere representation is used to encode a simple planar patch approximation of the objects' surface and to validate mapping between the measurements collected from different viewpoints. The technique also makes use of the compact surface representation to successively estimate the rotation and the translation parameters between sensor viewpoints. This solution results in an important reduction of the computational workload and provides sufficient accuracy for most robot navigation applications. The proposed approach performances are demonstrated in an experimental context using real range measurements collected from a series of viewpoints.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it