A higher order Rao-Blackwellized particle filter for monocular vSLAM
Why this work is in the frame
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Bibliographic record
Abstract
This paper generalizes the traditional formulation of Rao-Blackwellized particle filter (RBPF) by incorporating a higher order state variable and a modified undelayed initialization scheme to solve the 3D monocular visual SLAM problem (vSLAM). In the proposed approach, velocity has been included in the state variables so that filtering progresses based on sampling from velocity distribution, not the displacement. The new sampling posterior has been obtained with respect to observations, control inputs and the robot path. The proper importance weight for resampling has been derived in this paper. To solve the bearing-only problem, the proposed approach features a modified initialization scheme that uses an inverse depth of the landmarks. The proposed higher order RBPF approach has been compared to the traditional (lower order) RBPF approach for proof of concept through a tangible simulation routine. The results of the numerical simulation indicate the superiority of the higher order RBPF in certain conditions e.g., high parallax angles.
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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) | 0.001 | 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 it