Enhanced pedestrian attitude estimation using vision aiding
Bibliographic record
Abstract
Inertial Navigation System (INS) sensors are widely used for augmenting Global Navigation Satellite System measurements in urban environments and in the indoors. With a known initial position, the current position may be propagated using gyroscopes and accelerometers forming the INS for a limited time. The limitation of the self-contained sensors is the cumulative measurement errors that affect the accuracy of the attitude obtained using the gyroscopes. Vision aiding has proven to be a feasible method for mitigating these errors. This paper introduces a method to obtain attitude measurements by tracking the motion of vanishing points in consecutive images and integrating these measurements with the attitude observed by INS using an extended Kalman filter. The experiments show that vision aiding results in significant improvement of the user attitude and therefore the navigation solution. The challenges in vanishing point-based vision aiding are the processing time and the method's lack of capability to perceive sharp turns. These issues are addressed by developing an algorithm based on the Probabilistic Hough Transform for more efficient vanishing point calculation which also provides a means for turn detection. These improvements advance the objective of developing a real-time seamless indoor–outdoor pedestrian navigation system utilising vision aiding.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".