Tracking and Estimation of a Swaying Payload Using a LiDAR and an Extended Kalman Filter
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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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".