Using multiple view geometry within extended Kalman filter framework for simultaneous localization and map-building
Why this work is in the frame
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Bibliographic record
Abstract
One of the recent and consistently interesting topics in robotics research community is the simultaneous localization and map-building (SLAM) problem. It examines the ability of an autonomous mobile vehicle starting in an unknown environment to incrementally build an environment map and simultaneously localize its pose within this map. In this paper, we present a solution to the SLAM problem with minimal initial knowledge. The novelty lies in its monocular vision sensing system, which uses a multiple view geometry (MVG) approach within an extended Kalman filter (EKF) framework. The MVG algorithm provides accurate structure and motion measurements from a monocular camera whereas traditional vision-based approaches require stereo-vision. It is evident from simulation results that the limitations of MVG and EKF, when used on their own are overcome in the proposed solution.
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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) | 0.000 | 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 it