Batch heterogeneous outlier rejection for feature-poor SLAM
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
In this paper, the problem of outliers in a batch alignment problem (given heterogeneous measurements and sparse features) is considered. The conventional approach from the field of computer vision, pairwise RANSAC, is shown to be inappropriate for this scenario, which motivates the need for a new method. To address this problem, the heterogeneous measurements are compared in a common currency using their respective scaled measurement innovations. Furthermore, a family of three algorithms for classifying outliers given a hypothesis model are presented, each having its own balance between speed and accuracy. These classification criteria are then incorporated through iterative reclassification in a batch alignment framework, providing a robust estimate for localization and mapping. Lastly, statistical validation is obtained through a large set of simulated trials.
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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