MétaCan
Menu
Back to cohort
Record W2153565898 · doi:10.1109/imtc.1997.604018

3D data acquisition for indoor environment modeling using a compact active range sensor

2002· article· en· W2153565898 on OpenAlex
S. Elgazzar, Ramiro Liscano, F. Blais, Andrew Miles

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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsComputer scienceVisualizationRange (aeronautics)Tilt sensorReal-time computingComputer visionRemote sensingArtificial intelligenceEngineeringGeographyTelecommunications

Abstract

fetched live from OpenAlex

This paper investigates modeling indoor environments using a low-cost compact active range camera, known as BIRIS, mounted onto a pan and tilt motor unit. The BIRIS sensor, developed at the National Research Council of Canada, is a rugged small camera with no moving parts. The contributions of this paper are mainly in three areas: it demonstrates the viability of the use of a low-cost range sensor in the domain of indoor environment modeling; it presents the of processing 3-D data to build a virtual environment for navigation and visualization; and, it analyses and outlines the advantages and and limitations encountered when scanning large indoor 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 score0.892
Threshold uncertainty score0.481

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 head0.096
GPT teacher head0.251
Teacher spread0.155 · 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

Quick stats

Citations3
Published2002
Admission routes3
Has abstractyes

Explore more

Same topicRobotics and Sensor-Based LocalizationFrench-language works237,207