Efficient Augmentation of the EKF Structure from Motion with Frame-to-Frame Features
Bibliographic record
Abstract
The Extended Kalman Filter (EKF) is still one of the most widely used approaches for small scale Structure from Motion (SFM) and Simultaneous Localization And Mapping (SLAM) problems. However, the EKF does not have the ability to take into account the motion information carried by features matched only between two consecutive frames. This information is valuable because, when used appropriately, it generally enhances the performance of the filter. Two main reasons hinder the direct use of such features in the EKF: their un-initialized 3D location would corrupt the covariance matrix, and the computational cost grows cubically with the number of features. In this paper we present a novel approach to solve those problems. Our approach folds the frame-to-frame information in the filter through a separate update step that can be carried out in linear time. Other advantages of our approach is that it can be introduced to already implemented filters with minimal change. It can be done in a separate thread to further speedup the computation. Additionally, it can be further divided to multiple steps with different sets of features, which permits to reject or accept each step based on some performance criteria and to stay within the budgeted time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".