Fundamental Study on Evacuation Guidance Assistance Robots in Disasters: A Measurement Method for Evacuation Routes Using 3D-LiDAR
Bibliographic record
Abstract
This study explores a methodology for mapping the 3D spatial information of evacuation routes, aiming to acquire lightweight data to enhance the flexibility of evacuation route development.Utilizing 3D-LiDAR (Light Detection and Ranging) technology, we integrated an Inertial Measurement Unit (IMU) with an existing algorithm to compensate for motion distortion, thereby increasing the mapping accuracy of interior spaces characterized by a paucity of structural features.Given the extensive volume of point cloud data generated by LiDAR, which is impractical for direct application in evacuation route mapping, we categorized the data into local and global maps.This paper presents a strategy to minimize the data volume necessary for generating optimized evacuation route maps, employing the Octomap data format for efficient global map storage.This approach not only addresses the challenges of handling large datasets but also contributes to the development of more accurate and user-friendly evacuation planning tools.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".