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Record W2166176559 · doi:10.1109/crv.2011.59

Precise High Speed Multi-Target Multi-Sensor Local Positioning System

2011· article· en· W2166176559 on OpenAlex
Justin A. Eichel, David A. Clausi, Paul Fieguth

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInertial measurement unitComputer visionComputer scienceArtificial intelligenceVideo trackingTracking systemOrientation (vector space)Global Positioning SystemLow latency (capital markets)Robustness (evolution)Object (grammar)Kalman filterMathematics

Abstract

fetched live from OpenAlex

When used for tracking, the combination of infrared (IR) and an internal measurement unit (IMU) allows researchers and industry to locate objects to within 1 cm at over 200 Hz with a latency less than 2 ms. This novel tracking system uses multiple cameras to triangulate an IR LED placed on the object and utilizes IMU data to measure the object's orientation and allow the system to be robust against visual occlusions. The cost-effective IR system is robust against illumination allowing the object to be tracked at over 50 feet from the camera positions. This distance is expected to increase with additional development. This paper describes the preliminary algorithms that utilize the information from both the IR and IMU systems in order to precisely track an object. The algorithms are tested against simulated data generated from profiling the hardware and real data collected from a system prototype.

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: Methods · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score0.726

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 head0.027
GPT teacher head0.203
Teacher spread0.176 · 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

Citations2
Published2011
Admission routes2
Has abstractyes

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