A Jacobian free approach for multi-robot relative localization
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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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".