Likelihood-based iterated cubature multi-state-constraint Kalman filter for visual inertial navigation system
Bibliographic record
Abstract
In this paper, we present an advanced real-time Visual Inertial Navigation System (VINS) based on Multi-State Constraint Kalman Filter (MSCKF). This filter uses Cubature Kalman Filter (CKF) for nonlinear measurement update and Maximum Likelihood Estimate (MLE) to optimize the estimate, which in turn provides better system accuracy and stability. The measurement model is developed basing Trifocal Tensor Geometry (TTG), which allows replacing the 3D feature-point reconstruction step as in traditional VINS systems. Alternatively the available Unscented MSCKF [1] based on Unscented Kalman Filter has an implementation issue of executing the square-root operation of the covariance matrix due to the negatively-weighted sigma points, and this may halt the filter operation or even causes the system to fail. The proposed CKF structure has the ability to carry the highly-nonlinear TTG-based measurement model as well as overcome the issue associated with the covariance square-root operation. The MLE based iteration is applied to optimize the visual measurement update where it performs multiple corrections on a single measurement. This procedure helps to minimize the error accumulation allowing the filter to operate for longer durations. The proposed Iterated Cubature MSCKF is tested using KITTI datasets [2] and compared against the Unscented MSCKF and non-iterated Cubature MSCKF.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".