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Record W2079517579 · doi:10.1109/crv.2014.13

Autonomous Maritime Landings for Low-Cost VTOL Aerial Vehicles

2014· article· en· W2079517579 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 Waterloo
Fundersnot available
KeywordsComputer scienceInertial measurement unitGlobal Positioning SystemComputer visionInertial frame of referenceFrame (networking)Real-time computingArtificial intelligencePoseAccelerometerSimulationMarine engineeringEngineering

Abstract

fetched live from OpenAlex

Autonomous landing of quad rotor UAV on a maritime vessel is a challenging task, as low cost sensors, unknown movements of the landing surface, and external disturbances make it difficult to generate a relative pose estimate with sufficient accuracy for landing. In this work, we propose an architecture that avoids sensor limitations while allowing for accurate relative pose estimation, even in the presence of wind disturbances. The final landing sequence is performed entirely in the body-fixed inertial frame so that noisy measurements from the GPS and magnetometer sensors do not degrade the relative estimation accuracy. Simulation results of the entire system architecture are presented, as well as experimental results of visual landing pad tracking for representative motions, which demonstrate the validity of the approach.

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 score0.916
Threshold uncertainty score0.323

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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

Citations22
Published2014
Admission routes1
Has abstractyes

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