GPS/Reduced IMU with a Local Terrain Predictor in Land Vehicle Navigation
Bibliographic record
Abstract
In order to reduce the cost and volume of land vehicle navigation (LVN) systems, a “reduced” inertial measurement unit (IMU) consisting of only one vertical gyro and two or three accelerometers is generally used and is often integrated with other sensors. Since there are no horizontal gyros in a reduced IMU, the pitch and roll cannot be calculated or observed directly from the inertial data, and the navigation performance is thus affected by local terrain variations. In this work, a reduced IMU is integrated with global positioning system (GPS) data and a novel local terrain predictor (LTP) algorithm. The latter is used primarily to help estimate the pitch and roll of the reduced IMU system and thus to improve the navigation performance. In this paper, two reduced IMU configurations and two grades of IMUs are investigated using field data. Test results show that the LTP is valid. Specifically, inclusion of the LTP provides more than an 80% horizontal velocity improvement relative to the case when the LTP is not used in a GPS/reduced IMU configuration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".