Investigating the Performance of Corridor and Door Detection Algorithms in Different Environments
Why this work is in the frame
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Bibliographic record
Abstract
The capability of identifying physical structures in an unknown environment is important for autonomous mobile robot navigation and scene understanding. A methodology for detecting corridor and door structures in an indoor environment is proposed, and the performances of the corridor detection algorithm and door detection algorithm applied in different environments are evaluated. In the proposed algorithms, we utilize a feedback mechanism based hypothesis generation and verification (HGV) method to detect corridor and door structures using low level line features in video images. The proposed method consists of low, intermediate, and high level processing stages which correspond to the extraction of low-level features, the formation of hypotheses, and the verification of hypotheses using a feedback mechanism, respectively. The system has been tested on a large number of real corridor images captured by a moving robot in a corridor. The experimental results validated the effectiveness and robustness of the proposed methods with respect to different viewpoints, different robot moving speed, under different illumination conditions and reflection variations.
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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