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Record W1963565453 · doi:10.1109/tmech.2011.2159388

3-D Active Sensing in Time-Critical Urban Search and Rescue Missions

2011· article· en· W1963565453 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2011
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUrban search and rescueSearch and rescueRescue robotComputer scienceRobustness (evolution)RubbleArtificial intelligenceComputer visionRobotMobile robotReal-time computingGeography

Abstract

fetched live from OpenAlex

Mobile robots are currently being developed to help rescue workers in urban disaster environments to search for survivors. Our research focuses on developing robust 3-D sensors that can be used in robotic rescue missions to map these unknown cluttered environments and determine the locations of victims. This paper presents the development of a new active 3-D sensory system for robotic search and rescue in unknown cluttered urban disaster environments. The sensory system can provide high resolution 2-D and 3-D information of a cluttered scene that can be used by a robot operator for real-time viewing as well as to develop a 3-D map of the disaster scene. The main advantage of the proposed sensory system is its robustness to cluttered, and dark and dimly lit environments found in disaster areas. Experimental results are presented to verify the performance of the sensory system in obtaining 3-D sensory information of rubble piles in Urban Search and Rescue like environments as well as the potential use of the sensor for 3-D mapping applications in these unknown environments.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.233
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it