State Covariance Based Spatio-Temporal Trajectory Alignment for VIO Systems
Bibliographic record
Abstract
Abstract. With the growing interest in robotics and autonomous vehicles, visual-inertial odometry (VIO) in SLAM techniques have become increasingly important for estimating a robot’s trajectory using visual and inertial data. Evaluating the accuracy of a VIO trajectory estimate typically requires aligning it with a reference trajectory, where the goal is to find a transformation that minimizes the discrepancy between estimated and reference poses. However, existing methods often overlook the state covariance of the estimated trajectory or rely solely on manufacturer specifications, without considering the covariance of VIO estimation. As a result, the accuracy of the aligned trajectory may not be truly reflected, as the alignment process minimizes global discrepancies rather than prioritizing state pairs with higher confidence. This paper investigates the impact of state covariance of estimated trajectory on spatio-temporal trajectory alignment. To validate our approach, we use various open-source datasets that exhibit different state covariance behaviors and conduct comprehensive statistical analyses. The results indicate that the proposed method improves the precision and internal reliability in estimating alignment parameters. Additionally, the resulting a-posteriori variance factor of unit weight from the proposed method reflects a better-calibrated stochastic model and can serve as an indicator of state covariance estimation quality in VIO systems.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".