Corridor Line Detection for Vision Based Indoor Robot Navigation
Bibliographic record
Abstract
The capability of a mobile robot to negotiate corridors is essential for autonomous navigation in an indoor environment. An approach is proposed for determining the corridor line locations and the vanishing point in a corridor environment using a single camera, based on hypotheses generation/verification and a feedback control strategy. A corridor line is the intersection line between a wall and the floor, which is, the farthest lateral position the autonomous robot can safely navigate in a corridor. There have been numerous approaches described in the literature which detect corridor edges and vanishing point; however, no solution has been reported to detect true corridor line locations in the presence of many spurious linear features around the corridor line. The proposed method consists of low, medium, and high level processing stages which correspond to the extraction of 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 demonstrated the reliability and robustness of the approach with respect to different viewpoints, reflection variations and different illumination conditions
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".