Self-Localization System for Robots Using Random Dot Floor Patterns
Bibliographic record
Abstract
Self-Localization System for Robots Using Random Dot Floor Patterns Yutaro Fukase, Hiroshi Kanamori, Shinich Kimura Pages 304-312 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Various types of service robots have recently been developed for guarding facilities, caring for the elderly, carrying objects, and cleaning buildings. As barrier-free facilities improve and their use expands, these robots have more space within which to move inside buildings. Yet robots that move autonomously rely on position-detection systems. Though improving rapidly, these systems are far from perfect in determining positions in certain situations, especially when robots navigate large areas or cross various locations. Our group is working to solve this problem by developing a position-detection system using random-dot patterns on a floor. First, we construct a floor with a random-dot pattern and register the positions of all of the dots into a database. As the robot moves across the floor, a camera on the robot captures an image of the floor beneath it and crops the dot pattern in the image. The cropped dot pattern is matched to the dot patterns in the database to determine the position of the robot and the direction in which the robot is facing or moving. In this paper we propose a self-localization system and matching algorithms derived from a space technology and present the results of several experiments. Keywords: Self-localization system, Matching algorithm, Space technology DOI: https://doi.org/10.22260/ISARC2013/0033 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".