Vision data registration for robot self-localization in 3D
Why this work is in the frame
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Bibliographic record
Abstract
We address the problem of globally consistent estimation of the trajectory of a robot arm moving in three dimensional space based on a sequence of binocular stereo images from a stereo camera mounted on the tip of the arm. Correspondence between 3D points from successive stereo camera positions is established through matching of 2D SIFT features in the images. We compare three different methods for solving this estimation problem, based on three distance measures between 3D points, Euclidean distance, Mahalanobis distance and a distance measure defined by a maximum likelihood formulation. Theoretical analysis and experimental results demonstrate that the maximum likelihood formulation is the most accurate. If the measurement error is guaranteed to be small, then Euclidean distance is the fastest, without significantly compromising accuracy, and therefore it is best for on-line robot navigation.
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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