Stereo Visual Odometry With Automatic Brightness Adjustment and Feature Tracking Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Vision-based localization and mapping can be easily affected by unstable feature tracking and illumination variations. To address these problems, we propose a point-based stereo visual odometry (VO) system with image brightness adjustment and feature tracking prediction. The system incorporates two threads that run in parallel: front-end and back-end. The front-end thread performs brightness adjustment, feature tracking, and motion estimation between frames. When the brightness of image changes significantly, a cumulative gray-scale histogram is used to estimate the exposure of the camera and adjust the brightness of the image. Additionally, a constant acceleration motion model and stereo geometric constraint are used to predict the location of feature points in the target image, providing a reliable initial guess for the Lucas–Kanade (LK) optical flow tracker. In order to improve the accuracy and reduce computational complexity, the back-end performs a sliding window bundle adjustment (BA) to achieve optimal camera poses and landmark positions. Experiments on publicly available datasets indicate that the proposed scheme has a better performance than state-of-the-art stereo VO.
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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