Point Me In The Right Direction: Improving Visual Localization on UAVs with Active Gimballed Camera Pointing
Bibliographic record
Abstract
Robust autonomous navigation of multirotor UAVs in GPS-denied environments is critical to enable their safe operation in many applications such as surveillance and reconnaissance, inspection, and delivery services. In this paper, we use a gimballed stereo camera for localization and demonstrate how the localization performance and robustness can be improved by actively controlling the camera's viewpoint. For an autonomous route-following task based on a recorded map, multiple gimbal pointing strategies are compared: off-the-shelf passive stabilization, active stabilization, minimization of viewpoint orientation error, and pointing the camera optical center at the centroid of previously observed landmarks. We demonstrate improved localization performance using an active gimbal-stabilized camera in multiple outdoor flight experiments on routes up to 315m, and with 6-25m altitude variations. Scenarios are shown where a static camera frequently fails to localize while a gimballed camera attenuates perspective errors to retain localization. We demonstrate that our orientation matching and centroid pointing strategies provide the best performance; enabling localization despite increasing velocity discrepancies between the map-generation flight and the live flight from 3-9m/s, and 8m path offsets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".