Motion Constraints and Vanishing Point Aided Land Vehicle Navigation
Bibliographic record
Abstract
In the typical Inertial Navigation System (INS)/ Global Navigation Satellite System (GNSS) setup for ground vehicle navigation, measures should be taken to maintain the performance when there are GNSS signal outages. Usually, aiding sensors are utilized to reduce the INS drift. A full motion constraint model is developed allowing the online calibration of INS frame with respect to (w.r.t) the motion frame. To obtain better heading and lateral positioning performance, we propose to use of vanishing point (VP) observations of parallel lane markings from a single forward-looking camera to aid the INS. In the VP module, the relative attitude of the camera w.r.t the road frame is derived from the VP coordinates. The state-space model is developed with augmented vertical attitude error state. Finally, the VP module is added to a modified motion constrains module in the Extended Kalman filter (EKF) framework. Simulations and real-world experiments have shown the validity of VP-based method and improved heading and cross-track position accuracy compared with the solution without VP. The proposed method can work jointly with conventional visual odometry to aid INS for better accuracy and robustness.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".