Integrated INS/GPS System for an Autonomous Mobile Vehicle
Bibliographic record
Abstract
This paper presents the design of an extended Kalman filter (EKF) to fuse GPS and MEMS-based inertial measurement unit measurements (IMU) for real-time state estimation of a high performance vehicle called CENTAUR. To fully enable its autonomous control capability, the navigation system requires estimates of the vehicle position, velocity and heading angle. Also, to control stability of the vehicle during more difficult maneuvers, estimation of bank angle as well as pitch angle should be provided. The estimation problem is complicated by the large random-walk error, along with vehicle's wide-band vibration. By combining the IMU data with GPS position measurements, the position estimation is adequate, and by incorporating Centaur's kinematics model (and using its stepper motors control input signals), the velocity and orientation estimation effectively improves. The filter represents quaternion rather than Euler angles which eliminates the problem of singularities and relatively high required computation time associated with attitude estimation. Both computer simulation results and real-time experimental results show that integrated system has better long-term precision than a low-cost INS alone, and also better than a stand GPS in terms of availability and continuity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".