Development of a relative localization scheme for ground-aerial multi-robot systems
Bibliographic record
Abstract
In this paper we demonstrate a design and experimentation of a relative localization solution for a multi robot team involving both ground and aerial robots. The relative localization method proposed in this paper has the ability to localize a dynamic agent with respect to only one leader ground robot in a GPS denied environment. The sensor solution proposed in the study employs a combination of an acoustic sensor and an infra-red(IR) based vision sensor for relative range and bearing estimations respectively. An extended Kalman filter performs the sensor fusion using a four degree of freedom kinematic model. Numerical simulations validate the sensor fusion scheme for both ground and aerial robotic relative localization. An experimental test-bed of the system with the hardware implementation of the sensors were developed. For comparison purposes the self localization modules of the robots are further integrated into the experimental setup. Realtime experiments were performed where 5-10 cm mean accuracy of pose estimation was achieved in multiple experiments.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".