Attitude determination and localization of mobile robots using two RTK GPSs and IMU
Bibliographic record
Abstract
This paper focuses on the design and test results of an adaptive variation of Kalman filter (KF) estimator based on fusing data from Inertial Measurement Unit (IMU) and two Real Time Kinematic (RTK) Global Positioning Systems (GPS) for driftless 3-D attitude determination and robust position estimation of mobile robots. GPS devices are notorious for their measurement errors vary from one point to the next. Therefore in order to improve the quality of the attitude estimates, the covariance matrix of measurement noise is estimated in real time upon information obtained from the differential GPS measurements, so that the KF filter continually is ¿tuned¿ as well as possible. No a priori knowledge on the direction cosines of the gravity vector in the inertial frame is required as these parameters can be also identified by the KF, relieving any need for calibration. Next, taking advantage of the redundant GPS measurements, a weight least-squares estimator is derived to weight the GPS measurement with the ¿good¿ data more heavily than the one with ¿poor¿ data in the estimation process leading to a robust position estimation. Test results are presented showing the performance of the integrated IMU and two GPS to estimate the attitude and location of a mobile robot moving across uneven terrain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".