A Fuzzy Set-Theoretic Approach to Occupancy Grid Mapping
Bibliographic record
Abstract
This paper introduces a fuzzy set-theoretic approach to environment modeling as an alternative to conventional Occupancy Grid Mapping (OGM). The proposed method generalizes the crisp definitions of map cells and sensor measurements by employing fuzzy sets. Consequently, it utilizes fuzzy inference with interpretable rules instead of binary Bayesian inference to determine occupancy states from sensory data. As a result, the fuzzy mapping technique addresses uncertainty while eliminating assumptions about data distribution or cell occupancy probabilities. A notable advantage of the proposed Fuzzy Occupancy Grid Mapping (FOGM) is its ability to evaluate the overall occupancy states of sub-regions of the environment in a single query, unlike traditional methods that require point-by-point assessments. While this advantage introduces increased computational complexity, the mapping process is designed to ensure real-time operation. Another prominent feature of the FOGM is that it creates continuous maps with smooth occupancy variations as compared to the discrete binary maps the traditional OGM generates. The performance of the proposed technique is validated through experiments involving a mobile robot localized via a motion capture system. The robot collects range data in real-time using a LiDAR sensor to be used for mapping. Results demonstrate that the fuzzy mapping approach effectively and accurately represents the environment while maintaining computational efficiency. Its capability in parallel execution on multiple processing cores further enhances real-time performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".