Bibliographic record
Abstract
A vision system is developed for a mobile cleaning robot to detect orientation, which can be used alone or with predicted motion to reduce localization error. By exploiting straight line features found in ceilings with suspended tiles, orientation is found in realtime with a desktop PC implementation. Simple techniques are applied to achieve realtime performance in a system which is suitable for implementation in an embedded system or DSP. Edge strengths and directions are first calculated. Points potentially belonging to line features are then found by applying a dynamically calculated global threshold designed to retain a fixed percentage of edge points, and the application of an edge thinning operation which implements a fast peak detection algorithm. The remaining edge points are then used to determine an initial orientation estimate. Orientations are found by detecting four peaks separated by 90/spl deg/ intervals in a contour-direction histogram. The orientation value is further refined by rejecting points which are not close to the main orientation estimate, and by removing points which are part of very short lines resulting from texture patterns rather than long straight line features. The theoretical basis, system design and prototype implementation, testing, and evaluation are described. The experimental results of integrating a prototype system with an experimental mobile robot are included.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".