Occupancy grid mapping with Markov Chain Monte Carlo Gibbs sampling
Why this work is in the frame
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Bibliographic record
Abstract
Occupancy grids have been widely used for mapping with mobile robots for nearly 30 years. Occupancy grids discretize the analog environment and seek to determine the occupancy probability of each cell. Traditional occupancy grid mapping methods make two assumptions for computational efficiency and it has been shown that the full posterior is computationally intractable without these assumptions. This paper employs a form of Markov Chain Monte Carlo (MCMC) known as Gibbs sampling to sample from the full posterior. By drawing many samples, we are able to capture the full posterior, which more accurately represents the uncertainty in the map due to sensor measurement error. The MCMC method is shown to compute the full posterior in a 1D toy example, and it is shown to be computationally tractable, though not online, for realistic 2D simulations.
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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 | 0.000 |
| 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