Developing a Cubature Multi-state Constraint Kalman Filter for Visual-Inertial Navigation System
Bibliographic record
Abstract
The objective of this paper is to develop a cubature Multi-State Constraint Kalman Filter (MSCKF) for a Visual-Inertial Navigation System (VINS). MSCKF is a tightly-coupled EKF-based filter operating over a sliding window of multiple sequent states. In order to decrease the complexity and the computational cost of the original EKF-based measurement, the measurement model is built on the Trifocal Tensor Geometry (TTG). The predicted measurement does not need to reconstruct the 3D position of the visual landmarks. In order to employ that nonlinear TTG-based measurement model, this paper will implement cubature approach (i.e. popularly associated with Cubature Kalman Filter (CKF)). Compared to other advanced nonlinear filter, specifically Unscented Kalman Filter (UKF), the CKF has removed the positive-definite condition of the covariance matrix computation, which may halt or fail the filter operation. The proposed filter is validated with three KITTI datasets [1] of residential area to evaluate its performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".