Mapping with MAV: Experimental Study on the Contribution of Absolute and Relative Aerial Position Control
Bibliographic record
Abstract
Abstract. This study highlights the benefit of precise aerial position control in the context of mapping using frame-based imagery taken by small UAVs. We execute several flights with a custom Micro Aerial Vehicle (MAV) octocopter over a small calibration field equipped with 90 signalized targets and 25 ground control points. The octocopter carries a consumer grade RGB camera, modified to insure precise GPS time stamping of each exposure, as well as a multi-frequency/constellation GNSS receiver. The GNSS antenna and camera are rigidly mounted together on a one-axis gimbal that allows control of the obliquity of the captured imagery. The presented experiments focus on including absolute and relative aerial control. We confirm practically that both approaches are very effective: the absolute control allows omission of ground control points while the relative requires only a minimum number of control points. Indeed, the latter method represents an attractive alternative in the context of MAVs for two reasons. First, the procedure is somewhat simplified (e.g. the lever-arm between the camera perspective and antenna phase centers does not need to be determined) and, second, its principle allows employing a single-frequency antenna and carrier-phase GNSS receiver. This reduces the cost of the system as well as the payload, which in turn increases the flying time.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".