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Record W2114187164 · doi:10.1109/cccrv.2004.1301468

Stereo vision algorithm for robotic assembly operations

2004· article· en· W2114187164 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsArtificial intelligenceComputer visionEpipolar geometryComputer scienceStereo cameraTranslation (biology)TriangulationOrientation (vector space)Camera matrixComputer stereo visionStereopsisPinhole camera modelAlgorithmCamera resectioningMathematicsCamera auto-calibrationImage (mathematics)

Abstract

fetched live from OpenAlex

A stereo vision Linear Triangulation (LT) algorithm can be utilized in space robotics assembly operations. The LT algorithm recovers the relative orientation and translation (pose) of objects marked with high contrast targets using two or more pinhole charge-coupled device (CCD) cameras. The cameras view a set (including a disjoint set) of targets measured with respect to the same point in space. This study evaluates the theoretical accuracy of the LT algorithm, its benefits and performance. The introduction of a third camera into the vision system envelope is also examined and discussed. Experiments indicated that most accuracy in pose estimation is gained by the first 15-25 degrees of camera separation, and then the decrease in values of the covariance matrix elements stabilizes. We compare the numerical data for singe, two-camera and threecamera cases using extensive experiments on simulated images.

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: none
Teacher disagreement score0.670
Threshold uncertainty score0.303

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 head0.011
GPT teacher head0.243
Teacher spread0.231 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

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