Application of Segmented 2-D Probabilistic Occupancy Maps for Robot Sensing and Navigation
Bibliographic record
Abstract
The concept of probabilistic occupancy maps was introduced by the end of the 1980s. Over the years, research has focused on the definition of the representation, the data fusion, and the generation of such occupancy models. However, few considerations have been given to processing occupancy maps as textured images to extract meaningful information that is required for robot navigation. This paper investigates the application of modern segmentation techniques over 2-D probabilistic occupancy maps that are encoded as textured images. Enhancements are proposed to a uniformity estimation technique based on local binary pattern and contrast (LBP/C) to achieve the robust segmentation of occupancy maps that typically result from range sensors with limited resolution. The enhanced LBP/C segmentation technique handles occupancy uncertainty and subdivides the space in regions that are characterized by three deterministic occupancy states, which are defined as free, unknown, and occupied. The approach is also extended to increase the number of classification levels, which provides the necessary flexibility to automatically select the regions that are characterized by a given range of occupancy states. The use of these extensions, along with the accuracy of the segmented 2-D occupancy maps, is first experimentally demonstrated on ground-based probabilistic grids for application in mobile robot navigation with collision avoidance. The potential of the proposed approach is also evaluated on aerial and satellite images for which it provides stable results and can find applications for unmanned aerial vehicle navigation.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".