ESLS: A Vision-Based Emergency Safe Landing System for UAVs
Bibliographic record
Abstract
Uncrewed Aerial Vehicles (UAVs) are increasingly used across a wide range of missions, including healthcare-related operations such as medical supply delivery and search and rescue (SAR). However, performing safe emergency landings remains a critical challenge, especially in GPS-denied or cluttered environments such as forests or disaster zones. This paper presents an Emergency Safe Landing System (ESLS) designed to support emergency descent in any mission context. ESLS integrates RTAB-MAP SLAM with visual-inertial odometry (VIO), combining data from an onboard IMU and RGB-D camera to enable real-time 3D mapping and localization. The system uses YOLO-v5 object detection fused with a binary occupancy map. This allows robust identification of unobstructed areas in dynamic and unstructured environments. ESLS supports two operational modes: (1) Emergency Safe Landing Zone Detection (ESLZD), which selects the safest available landing zone; and (2) Search-and-Rescue Mode (ESLZD-SAR), which prioritizes landing safely near a detected survivor. Simulation results in ArduPilot Gazebo show landing zone detection success rates of up to 98% and landing success rates of up to 96%, highlighting the system’s potential for reliable deployment in both standard and SAR-specific UAV operations.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".