Decentralized Cooperative Localization for Heterogeneous Multi-robot System Using Split Covariance Intersection Filter
Bibliographic record
Abstract
This study proposes the use of a split covariance intersection filter (Split-CIF) for decentralized multi-robot cooperative localization. In the proposed method each robot maintains a local extended Kalman filter to estimate its own pose in a pre-defined reference frame. When a robot receives pose information from neighbouring robots it employs a Split-CIF-based approach to fuse this received measurement with its local belief. For a team of N mobile robots, the processing and communication complexity of the proposed method is linear, O(N), with respect to the number of robots in the team. The proposed method does not demand for fully connected synchronous communication channels between robots and can work with any asynchronous and partially connected communication network. Additionally, the proposed method gives consistent state updates and is capable of handling independent and interdependent parts of the estimations separately. The numerical simulations presented validate the proposed algorithm. The simulation results demonstrate that the proposed algorithm is outperformed compared to single-robot localization algorithms and also demonstrate approximately the same estimation accuracy as a centralized cooperative localization approach but with reduced computational cost.
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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 | 0.001 | 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) | 0.001 | 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".