AI-based landing zone detection for vertical takeoff and land LiDAR localization and mapping pipelines
Bibliographic record
Abstract
This work describes a deep learning-based autonomous landing zone identification module for a vertical takeoff and landing vehicle. The proposed module is developed using LiDAR point cloud data and can be integrated into a visual LiDAR odometry and mapping pipeline implemented in the vehicle. “ConvPoint,” the top-performing neural network architecture in an online point cloud segmentation benchmark leaderboard at the time of writing, was chosen as the reference architecture. Semantic labeling of the datasets was done using the terrain geometry characteristics and manual adjustment of labels through visual observation. Point clouds captured by the Memorial University and online point cloud datasets were used to transfer-learn the neural network model and to evaluate the accuracy-runtime trade-off for the proposed pipeline. The selected neural network model generated accuracy values of 89.7% and 92.1% on two selected datasets, while it computed 3940.15 points per second and 3633.85 points per second to predict landing zone labels, respectively. Hyperparameter tuning was carried out to obtain a higher throughput with an update rate of 1 Hz for the landing zone map of the point cloud inputs from the visual LiDAR odometry and mapping pipeline. The proposed system is validated by evaluating its performance on three variations of point clouds. The results validate the accuracy-runtime trade-off of the proposed system and show that further optimization can improve performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".