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Record W2119709757 · doi:10.1109/icra.2011.5979613

A self-calibrating 3D ground-truth localization system using retroreflective landmarks

2011· article· en· W2119709757 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 institutionsUniversity of Toronto
Fundersnot available
KeywordsGround truthComputer scienceComputer visionTranslation (biology)Orientation (vector space)Artificial intelligenceSoftware deploymentExploitSimple (philosophy)ScannerScale (ratio)Laser scanningLaserOpticsMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

In this paper, we present an infrastructure-based ground-truth localization system suitable for deployment in large worksite environments. In particular, the system is low cost, simple-to-deploy, and is able to provide full six-degree of-freedom relative localization for three-dimensional laser scanners with centimetre-level accuracy in translation, and half degree accuracy in orientation. This system utilizes common laser scanner hardware, and exploits the fact that retroreflective material is easily identified based on the return intensity. This enables the use of simple rectangular signs placed around the scene as landmarks. An uncertainty model is presented that accounts for the shape of the landmarks, and a batch alignment algorithm is formulated that efficiently considers the structure of the problem. Lastly, characterization of the accuracy of the system is provided through small-scale testing in an indoor lab, and examples for a large-scale setup.

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 score0.945
Threshold uncertainty score0.615

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 head0.024
GPT teacher head0.202
Teacher spread0.178 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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