Development of an Italian low cost GNSS/INS system universally suitable for mobile mapping
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.
Bibliographic record
Abstract
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 photogrammihetry, road sign recognition, etc.) for both commercial and research purposes throughout the world. Many GNSSIINS 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 GNSSIINS 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), has 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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it