Isolation of Vibrations Transmitted to a LIDAR Sensor Mounted on an Agricultural Vehicle to Improve Obstacle Detection.
Bibliographic record
Abstract
Isolation of Vibrations Transmitted to a LIDAR Sensor Mounted on an Agricultural Vehicle to Improve Obstacle Detection.Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 55: 2.33-2.42.LIDAR (LIght Detection And Ranging) technology can be used on autonomous agricultural vehicles for guidance and obstacle detection purposes.However, the quality of LIDAR measurements can be affected by mechanical vibrations induced by the operation of these vehicles on uneven terrain.The objective of this study was to develop a stabilizing system and to evaluate its effectiveness at reducing the transmission of mechanical vibrations to a LIDAR sensor installed on an agricultural tractor for the purpose of reducing the positioning error of obstacles during field operation.Special support bars (S) and stabilization system (SS) were designed for a SICK LMS 291-S14 LIDAR sensor mounted on an agricultural tractor.The positioning error of the sensor was assessed in field experiments by determining the difference between the known location of obstacles and their corresponding estimated locations from the sensor measurements.Increasing tractor speed had a negative effect on the accuracy of the sensor with an increase in the positioning error of up to 27%.The addition of the S system positively affected the accuracy of the sensor and resulted in a 41% decrease of the average positioning error from 340 to 201 mm.Finally, the addition of the SS system decreased the average positioning error by 57% from 382 to 161 mm.
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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) | 0.000 | 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) | 0.001 | 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".