Three‐dimensional Scan Registration using Curvelet Features in Planetary Environments
Bibliographic record
Abstract
Topographic mapping in planetary environments relies on accurate three‐dimensional (3D) scan registration methods. However, most global registration algorithms relying on features such as fast point feature histograms and Harris‐3D show poor alignment accuracy in these settings due to the poor structure of the Mars‐like terrain, and the variable‐resolution, occluded, sparse range data that are difficult to register without somea prioriknowledge of the environment. In this paper, we propose an alternative approach to 3D scan registration using the curvelet transform that performs multiresolution geometric analysis to obtain a set of coefficients indexed by scale (coarsest to finest), angle, and spatial position. Features are detected in the curvelet domain to take advantage of the directional selectivity of the transform. A descriptor is computed for each feature by calculating the 3D spatial histogram of the image gradients, and nearest‐neighbor‐based matching is used to calculate the feature correspondences. Correspondence rejection using random sample consensus identifies inliers, and a locally optimal singular value decomposition‐based estimation of the rigid‐body transformation aligns the laser scans given the reprojected correspondences in the metric space. Experimental results on a publicly available dataset of a planetary analogue indoor facility, as well as simulated and real‐world scans from Neptec Design Group's IVIGMS 3D laser rangefinder at the outdoor CSA Mars yard, demonstrate improved performance over existing methods in the challenging sparse Mars‐like terrain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".