Performance evaluation of neural network based integration of vision and motion sensors for vehicular navigation
Bibliographic record
Abstract
The positioning accuracy of global navigation satellite systems (GNSS) in dense urban canyon environment significantly deteriorates due to multipath and signal blockage. For this reason, inertial navigation system (INS) is often integrated with GNSS to ensure a reliable navigation solution during such periods of GNSS signal outages. A low-cost navigation solution for land vehicles has been developed by integrating GNSS positioning solution with the measurements from the vehicle motion sensors (accelerometers and gyroscopes). The major drawback of the usage of these inertial sensors is its progressive error accumulation, where the gyroscope drift errors increase gradually, leading to an unusable position estimate, especially in the absence of GNSS updates. Navigation in GNSS-denied environment requires aiding INS with other exteroceptive sensors such as cameras to guarantee the continuity of reliable positioning updates. The estimation of the camera’s relative change in position and orientation over time is known as visual odometry (VO). A VO-based multisensor integrated navigation system is presented here to surmount the inaccuracy of GNSS in urban scenarios and the drifts of the motion sensors. To enhance the overall system accuracy of the VO-based integrated solution, this paper explores improving the positioning accuracy during GNSS outages by nonlinear modeling of the residual position errors using a neural network. The results show a significant accuracy improvement over relatively long GNSS 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".