ESLS: A Vision-Based Emergency Safe Landing System for UAVs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it