Consistent Fusion of Correlated Pose Estimates on Matrix Lie Groups
Bibliographic record
Abstract
Pose fusion on the Special Euclidean groups ($SE(n)$) plays a key role in the localization of ground/aerial vehicles. However, for consistent fusion of pose estimates, the enduring correlation problem due to the observation of a common noise-corrupted process is yet to be addressed. We develop a methodology for the consistent fusion of correlated pose estimates that optimizes a quadratic cost function encompassing both self- and cross-correlation of local pose estimates. The error terms are calculated relative to a reference estimate and approximated using the Baker-Campbell-Hausdorff formula to obtain a non-iterative solution on the Lie algebra. The dependency of the fusion on the cross-covariance matrices is addressed via explicitly computing them through a recursive propagation of estimation errors at local extended Kalman filters. The efficacy of the proposed methodology is demonstrated by several numerical experiments conducted to (i) rigorously investigate the effect of correlation degree between local estimates on$SE(3)$and (ii) solve the localization problem of a rover on$SE(2)$with available pseudo pose measurements.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".