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Record W2134373387 · doi:10.1109/plans.2004.1309003

A quaternion-based orientation estimation algorithm using an inertial measurement unit

2004· article· en· W2134373387 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
TopicInertial Sensor and Navigation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsQuaternionInertial measurement unitAccelerometerOrientation (vector space)Kalman filterEuler anglesComputer scienceGyroscopeInertial navigation systemEncoderExtended Kalman filterControl theory (sociology)Filter (signal processing)Representation (politics)YawAlgorithmComputer visionArtificial intelligenceEngineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

This paper presents a real-time orientation estimation algorithm based on signals from a low-cost inertial measurement unit (IMU). The IMU consists of three MEMS accelerometers and three MEMS rate gyros. This approach is based on relationships between the quaternion representing the platform orientation, the measurement of gravity from the accelerometers, and the angular rate measurement from the gyros. Process and measurement models are developed, based on these relations, in order to implement them into an extended Kalman filter. The performance of each filter is evaluated in terms of the roll, pitch, and yaw angles. These are derived from the filter output since this orientation representation is more intuitive than the quaternion representation. Extensive testing of the filters with simulated and experimental data show that the filters perform very accurately in the roll and pitch angles, and even significantly corrects the yaw angle error drift.

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 score0.539
Threshold uncertainty score0.393

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 head0.041
GPT teacher head0.266
Teacher spread0.226 · 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

Citations104
Published2004
Admission routes1
Has abstractyes

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