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POSITION AND ORIENTATION MEASUREMENT FOR AUTONOMOUS AERIAL REFUELING BASED ON MONOCULAR VISION

2017· article· en· W2586877469 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Robotics and Automation · 2017
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsOrientation (vector space)Computer visionMonocularArtificial intelligenceMonocular visionPosition (finance)Computer scienceMathematicsBusinessGeometry

Abstract

fetched live from OpenAlex

Combined with the 3D model of aerial refuelling drogue target, this paper proposes a method for measuring the position and orientation based on monocular vision. The shape of the drogue's inner dark part is circular and its image is a circle or an ellipse in the image space. The contour points of the dark part are extracted in the image space and the adaptive elliptical parameter extraction algorithm based on RANSAC is adopted to get the parameters of the ellipse in the image space. Based on the principle of pinhole imaging and the dimension of the drogue's dark part, a visual cone is established and the position and orientation of the drogue's inner dark part can be deduced by using the geometric relationships. The experiments for measuring the drogue's position and orientation are carried on in a platform composed of two KUKA robots, and the experiment results verify the effectiveness of the proposed method.

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 score0.870
Threshold uncertainty score0.267

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 head0.249
Teacher spread0.238 · 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