Wheel-based Aiding of Low-cost IMU for Land Vehicle Navigation in GNSS Challenging Environment
Bibliographic record
Abstract
Recently, Autonomous land vehicle navigation became an important research topic. Most of the land vehicle navigation systems are based on Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integrated system. However, this system doesn't efficiently work in some environments due to the GNSS signal outages and the deterioration of the navigation solution due to the large INS errors. Therefore, INS should be aided to limit its drift during GNSS signal blockage. This research proposes a multi low-cost INS configuration in land vehicle where two low-cost IMU sensors are mounted on the center of rear wheels of the land vehicle to estimate the vehicle's forward velocity through the gyroscopes located in the perpendicular direction of the wheel. A differential wheel odometry based on the Inertial Measurement Unit (IMU) mounted on the rear wheels is proposed to estimate the vehicle's change of heading. The proposed IMU wheel odometers are calibrated by providing GNSS/INS integrated forward velocity and heading change during GNSS signal availability. On the other hand, during GNSS signal outages, the IMU wheel based aiding system provides both velocity and heading change updates to the navigation filter to mitigate the large drift of the on-board IMU.Experimental tests have been implemented and the results show that the Root Mean Square Error (RMSE) of the IMU-based wheel odometer velocity is 0.08 m/sec while the RMSE of the typical odometer velocity obtained from On-Board Diagnostics II (OBD-II) is 0.26 m/sec. On the other hand, the RMSE of the estimated vehicle's heading change by the proposed differential wheel odometry reached 2 degrees/second for 360 second simulated GNSS signal outage. The navigation solution is enhanced when the IMU-based odometer velocity updates the navigation filter Extended Kalman Filter (EKF) and the average position RMSE reaches 4.96 meters, instead of 88.83 meters, for the INS standalone navigation solution during 60 seconds GNSS signal outage while the RMSE reaches 6.10 meters when OBD-II typical odometer is used as update. On the other hand, the RMSE reaches to 3.81 meters when both heading change and velocity updates estimated from the IMU-based odometer are used to aid the INS during GNSS signal outages.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".