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Record W2176413462 · doi:10.1117/12.397796

Range error analysis of an integrated time-of-flight, triangulation, and photogrammetric 3D laser scanning system

2000· article· en· W2176413462 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

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsTriangulationPhotogrammetryLaser scanningLaser rangingRange (aeronautics)Computer scienceArtificial intelligenceComputer visionError analysisLidarLaserRemote sensingOpticsGeologyGeographyEngineeringMathematicsAerospace engineeringCartographyPhysics

Abstract

fetched live from OpenAlex

A 3-D laser tracking scanner system analysis focusing on immunity to ambient sunlight and geometrical resolution and accuracy is presented in the context of a space application. The main goal of this development is to provide a robust sensor to assist in the assembly of the Space Station. This 3-D laser scanner system can be used in imagery or in tracking modes, using either time-of-flight (TOF) or triangulation methods for range acquisition. It uses two high-speed galvanometers and a collimated laser beam to address individual targets on an object. In the tracking mode of operation, we will compare the pose estimation and accuracy of the laser scanner using the different methods: triangulation, TOF (resolved targets), and photogrammetry (spatial resection), and show the advantages of combining these different modes of operation to increase the overall performances of the laser system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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