Autonomous post‐disaster indoor navigation and survivor detection using low‐cost micro aerial vehicles
Bibliographic record
Abstract
This paper introduces an innovative autonomous survivor detection pipeline tailored for low-cost micro aerial vehicles (MAVs) operating in post-disaster indoor environments. This consists of three main components: (1) a novel pipeline for survivor geotagging, which includes autonomous navigation, mapping, and detection of survivors using thermal images; (2) a navigation strategy to ensure complete thermal scanning coverage for survivor detection using low-cost commercial grade thermal camera; and (3) robust and accurate survivor detection using YOLOv8x and thermal imaging. To demonstrate the effectiveness of the proposed framework, first, the autonomous navigation algorithm is simulated in Robotic Operating System (ROS) and experimentally validated under different layouts. Second, the YOLOv8x algorithm is pretrained and achieves high accuracy. Finally, a real-world implementation was conducted with partially covered survivors in a simulated post-disaster environment. The results demonstrated the proposed pipeline can accurately map the layout of the environment and identify all survivors. This study demonstrates that affordable MAVs with basic thermal cameras can be effectively used to geotag survivors to support rescue missions during post-disaster events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".