A DRONE SYSTEM FOR AUTONOMOUS MAPPING FLIGHTS INSIDE A FOREST – A FEASIBILITY STUDY AND FIRST RESULTS
Bibliographic record
Abstract
Abstract. In recent years, drones have gained wide popularity in forest research and operational applications. Over the forest canopy, where Global Navigation Satellite Systems (GNSS) are available, the flights are already highly automated. However, under the canopy of dense forests, the flights still need active manual control by a human pilot due to missing GNSS signal and obstacles. The objective of this study was to design and implement a prototype of a drone autonomously flying inside a forest for future boreal forest research purposes by utilizing open-source algorithms. Based on a literature survey, EGO-Planner-v2 with VINS-Fusion localization and stereo-depth camera-based mapping was chosen as the base of the implemented prototype. The algorithms were first tested in a simulator and later a custom drone hardware was built to evaluate the performance and suitability in real boreal forest environments. The evaluation criteria for the performance were the success of the mission, the reliability of the obstacle avoidance, and the accuracy of the localization. Based on the results, the performance of the prototype was promising, but in dense forests, the sensing of small needleless branches and leafless understory vegetation needs to be improved to increase reliability. In a dense spruce forest, nine of 19 test flights were successful, when approximate flight distances varied between 35 m and 80 m. In the longest of those test flights, the error of the VINS-Fusion estimate of the trajectory length was approximately 1 m.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".