Tracking and Estimation of a Swaying Payload Using a LiDAR and an Extended Kalman Filter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A Ground Penetrating Radar (GPR) has become an important tool for remote sensing studies in the Arctic for numerous applications, such as imaging ice sheets, making avalanche predictions and measuring the snow to ground boundary which can be used to forecast freshwater supply. Flying the GPR over a remote terrain such as the Arctic allows access to otherwise inaccessible Arctic regions. This can be achieved by suspending the GPR from a drone. However, the flight stability may be impacted by the nonlinear motion of the GPR. Minimizing the motion of the suspended payload is key to obtaining a stable flight and requires an accurate estimate for the position of the payload. This study uses a Velodyne VLP-16™ Light Detection and Ranging (LiDAR) sensor to measure the position of a suspended payload and an Extended Kalman filter to obtain an accurate estimate of the position of the payload. An experiment was conducted on a stationary drone with a swinging cable-suspended payload to test the feasibility of the proposed tracking and estimation system. The experimental results are presented to show the efficacy of the proposed solution. Vicon motion capture system was used to provide truth measurements and verify the experimental results.
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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