Pseudo-linear measurement approach for heterogeneous multi-robot relative localization
Why this work is in the frame
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Bibliographic record
Abstract
The purpose of relative localization (RL) is to locate and track one or more robots in another moving robot body-fixed coordinate frame using relative range and/or bearing measurements. Most available RL methods assume known initial conditions at the first encounter of an arbitrary robot, and the tracking is then followed using an extended Kalman filter (EKF). In case of poor filter initialization, these EKF based methods sometimes cause instability or demand longer settling time. To overcome this issue, this paper proposes a pseudo-linear measurement (PM) based technique for RL where true nonlinear measurements are algebraically transformed into PM. The proposed RL scheme is tested in Monte Carlo simulations for a heterogeneous multi-robot system comprising both aerial and ground robots. Results demonstrate that the proposed method performs RL with 5~10 cm positional accuracy and 0.075~0.1 rad orientational accuracy. The performance of the PM based RL is then compared against traditional EKF based methods with unknown filter initialization. The results demonstrate that the proposed method able to achieve both the positional and orientational accuracy within 12 iterations, whereas the traditional methods requires more than 250 iterations to achieve the same accuracy. The experiment validation of the proposed method was performed and results are congruent with the simulations.
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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 | 0.000 | 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