Application of Segmented 2D Probabilistic Occupancy Maps for Mobile Robot Sensing and Navigation
Why this work is in the frame
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Bibliographic record
Abstract
The concepts of occupancy grids and probabilistic maps were introduced at the end of the eighties. Since then, research work focused mainly on the definition of the representation, data fusion and generation of occupancy models. Few consideration has been given to processing occupancy maps as textured images in order to extract meaningful information required for robot navigation and control of interactions with the environment. This paper investigates the application of segmentation techniques on probabilistic occupancy maps represented as textured images. Enhancements are proposed to a uniformity estimation technique based on local binary pattern and contrast (LBP/C) to achieve robust segmentation of occupancy maps that typically result from range sensors with limited resolution. The accuracy of the segmented 2D occupancy maps is demonstrated experimentally through an application on mobile robot navigation with collision avoidance
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it