Gaze Control for Active Visual SLAM via Panoramic Cost Map
Bibliographic record
Abstract
In this work, we aim to improve the positioning accuracy of the visual simultaneous localization and mapping (VSLAM) through actively controlling the gaze of the positioning camera mounted on an autonomous guided vehicle (AGV). A panoramic cost map (PCM)-based gaze control method (PGC) is proposed for the active VSLAM. Different from traditional method, a panoramic camera is added beside the positioning camera to aid the gaze control of the positioning camera. The panoramic camera is used to perceive the environment and evaluate the potential performance of each available orientation of the positioning camera. The evaluation of all the available orientations will make up a panoramic cost map. The cost map is then used to help the gaze control method to select an optimal target gaze for the positioning camera. In the calculation of the panoramic cost map, the effective factors of the VSLAM, such as feature points and moving objects, are taken into consideration. In the gaze control method, we also take into consideration of errors of the system, the time delay of the proposed method, and the velocity of the AGV. The test results in different scenes with different VSLAM algorithms show that the proposed method can improve the positioning accuracy of all the tested VSLAM algorithms compared to fixed camera gaze.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".