MétaCan
Menu
Back to cohort
Record W2133315771 · doi:10.1109/maes.2009.4839272

Italian low cost GNSS/INS system suitable for mobile mapping

2009· article· en· W2133315771 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueIEEE Aerospace and Electronic Systems Magazine · 2009
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsnot available
Fundersnot available
KeywordsGNSS applicationsGlobal Positioning SystemMobile mappingPhotogrammetryComputer scienceCadastreInertial navigation systemReliability (semiconductor)Inertial measurement unitReal-time computingEmbedded systemEngineeringSystems engineeringTelecommunicationsGeographyArtificial intelligenceOrientation (vector space)

Abstract

fetched live from OpenAlex

The first studies for the mobile mapping and creation of a vehicle for this kind of research was carried out by Canadian Researchers in the 1980s. Since then, these vehicles have been widely employed in several applications (road cadastre maps, terrestrial photogrammetry, road sign recognition, etc.) for both commercial and research purposes throughout the world. Many GNSS/INS vehicles which can be equipped in different ways with one or more GPS, inertial sensors, and one or several cameras, have been realized. A characteristic shared by most of these devices concerns the high costs of the sensors, of the realization, and of the maintenance. For this reason, a GNSS/INS system, that is suitable for any vehicle, made up of low-cost devices (two GPS receivers, an INS, and a camera rigidly placed on a metallic bar), have been designed and built by our research group. Two tests run at different velocities have been carried out to evaluate the reliability of the system. After a presentation of the system, the differences that were witnessed during the application of these calibration methods are explained herein.

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 categoriesMeta-epidemiology (narrow)
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 score0.667
Threshold uncertainty score1.000

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 head0.202
Teacher spread0.195 · 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