A Jacobian free approach for multi-robot relative localization
Why this work is in the frame
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Bibliographic record
Abstract
This study presents a relative localization (RL) approach for an multi-robotics system (MRS), in which a robot detects and tracks one or more robots in its body-fixed coordinate system. A square-root cubature Kalman filter (SCKF) is employed to track the teammates' relative pose based on the high-frequency egocentric sensory data and the low-frequency inter-robot relative measurements (IRRM). This IRRM data consists of the relative range and the relative bearing between the tracking robot and its teammates. A series of Monte-Carlo simulations for a heterogeneous multi-robotic system is presented to evaluate the proposed SCKF-based RL scheme for different measurement noise configurations and different measurement update rates. To assess how the proposed SCKF-based RL scheme improves relative pose estimation, a comparison with the EKF and the general cubature Kalman filter-based RL schemes through numerical simulations are presented. The results suggest that the proposed SCKF-based RL scheme is a promising solution for relative pose estimation when an exteroceptive sensory system has high measurement uncertainty and/or low measurement update rate.
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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 | 0.001 | 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