An efficient distributed data correspondence scheme for multi-robot relative localization
Bibliographic record
Abstract
This research addresses the problem of relative localization within a robot network possessing relative measurements between robots. The problem of correspondence is inherent to most multi-robot relative sensing methods, such as LiDAR, RADAR and vision based solutions. Multi-sensor multi-target tracking approaches addresses the problem of correspondence, when good prioris for initial poses of the sensing platforms are assumed. However the multi-robot relative localization problem differs from the classical multi-target tracking scenario due to; a) the unavailability of initial poses of sensing platforms, b) the existence of mutual measurements between the sensing platforms, and c) the measurement set being mixed with both known and unknown correspondences. To address these specific characteristics of multi-robot systems, this study proposes a distributed data correspondence architecture which performs multi-hypothesis estimation of the robot states. The proposed architecture is implemented on a multi-robot relative sensor configuration which possess range measurements with known data correspondence and bearing measurements with unknown data correspondence. The proposed distributed multi-robot localization method is capable of addressing measurement correspondence, noise, and measurement clutter effectively, while possessing inherent initialization and recovery capability from unknown poses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".