Two Key-Frame State Marginalization for Computationally Efficient Visual Inertial Navigation
Bibliographic record
Abstract
In this paper we perform a detailed evaluation of two key-frame state marginalization for visual inertial navigation filters to show that the method is significantly more computationally efficient than generic visual inertial odometry (VIO) methods while being sufficiently accurate for micro aerial vehicle (MAV) navigation. For this purpose, we use the EuRoC MAV dataset [1] for comparing the drift of MSCKF-Generic [2], MSCKF-Mono [3], MSCKF-Two way [4], and Two key-frame [5] VIO filters. The error state formulation of the two key-frame based and multi key frame based VIO is presented, then the drift, accuracy, and execution time of each filter is compared. The results indicate close to 90% faster execution of two key-frame based VIO algorithm on all datasets compared while having less than 3% drift in position for the total distance traversed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".