Robust monocular SLAM using one 3D point
Bibliographic record
Abstract
In this paper, a motion-model-free monocular SLAM algorithm is proposed for simultaneous localization and mapping of a robotic system. A monocular image sequence captured by a calibrated camera is the only input to the system, and robust and accurate frame-to-frame camera poses and a 3D map of the environment can be estimated automatically by the approach. The pose estimation method takes advantage of the epipolar geometry in structure from motion (SfM) to recover the rotation matrix and translation term of the camera, and one 3D reference point is used to recover the camera's translation distance. Then, a random sampling consensus (RANSAC) framework is employed to find the robust rotation matrix and translation vector, and a nonlinear optimization algorithm is applied to optimize the estimated rotation matrix and translation vector by minimizing the projection errors. Finally, a local bundle adjustment algorithm is performed to optimize the results. Extensive experimental evaluations demonstrate the effectiveness of the proposed monocular SLAM algorithm.
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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".