Simultaneous Localization and Mapping Solutions Using Monocular and Stereo Visual Sensors with Baseline Scaling System
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, SLAM systems are introduced using monocular and stereo visual sensors. The SLAM solutions are implemented in both indoor and outdoor. The SLAM samples have been taken in different modes, such as a straight line that enables us to measure the drift, in addition to the loop sample that is used to test the loop closure and its corresponding trajectory deformation. In order to verify the trajectory scale, a baseline method has been used. In addition, a ground truth has been captured for both indoor and outdoor samples to measure the biases and drifts caused by the SLAM solution. Both monocular and stereo SLAM data have been captured with the same visual sensors which in the stereo situation had a baseline of 20.00 cm. It has been shown that, the stereo SLAM localization results are 75% higher precision than the monocular SLAM solution. In addition, the indoor results of the monocular SLAM are more precise than the outdoor. However, the outdoor results of the stereo SLAM are more precise than the indoor results by 30%, which is a result of the small stereo baseline cameras. In the vertical SLAM localization component, the stereo SLAM generally shows 60% higher precision than the monocular SLAM results.
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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