Spherical Grid-Based IMU/Lidar Localization and Uncertainty Evaluation Using Signal Quantization
Why this work is in the frame
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Bibliographic record
Abstract
<h3>Abstract</h3> This paper describes the design, analysis, and experimental evaluation of a spherical grid-based localization algorithm that leverages quantization theory to bound navigation uncertainty. This algorithm integrates data from light detection and ranging (lidar) and inertial measuring units in an iterative extended Kalman filter to estimate the position and orientation of a moving vehicle. An analytical bound is derived from the vehicle’s state estimation error, which accounts for both random measurement noise and the loss of localization information caused by gridding. The performance of the proposed approach is analyzed and compared with that of a brute-force spherical grid-based method and a landmark-based method in an indoor environment, whereas an outdoor experiment verifies the practicality of the method in a realistic driving scenario.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it