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Record W2115675575 · doi:10.1002/rob.21415

A landmark‐bounded method for large‐scale underground mine mapping

2012· article· en· W2115675575 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

VenueJournal of Field Robotics · 2012
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsQueen's University
FundersUniversidad Nacional de Colombia
KeywordsLandmarkGlobal Positioning SystemOccupancy grid mappingComputer scienceScale (ratio)Bounded functionField (mathematics)Computer visionWork (physics)Artificial intelligenceGridOccupancyOdometryData miningEngineeringCartographyGeologyGeographyGeodesyCivil engineeringRobotMathematicsMobile robot

Abstract

fetched live from OpenAlex

Abstract A practical localization technology for underground drift networks—such as those excavated in the practice of underground mining—has yet to become commercially available. This paper focuses on the problem of mapping GPS‐deprived underground environments with the eventual goal of using these maps for navigation. Recent industry‐directed work in the creation of a landmark‐bounded occupancy grid mapping tool that combines odometry, scanning laser data, and sporadically placed passive RFID tags is described. Unlike other work, the suggested approach holds the philosophy that precise localization of the actual landmark locations is not necessary; rather, landmarks serve as a global means for partitioning the map. Successful field experiments were conducted in two underground environments, with the results used to conduct a basic analysis of the described method. © 2012 Wiley Periodicals, Inc.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.429
Threshold uncertainty score0.307

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 head0.020
GPT teacher head0.272
Teacher spread0.252 · 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