Application of Segmented 2-D Probabilistic Occupancy Maps for Robot Sensing and Navigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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