Bibliographic record
Abstract
This paper addresses the coupled tasks of constructing a spatial representation of the environment with a mobile robot using noisy sensors (sonar) and using such a map to determine the robot's position. The map is not meant to represent the actual spatial structure of the environment so much as it is meant to represent the major structural components of what the robot "sees". This can, in turn, be used to construct a model of the physical objects in the environment. One problem with such an approach is that maintaining an absolute coordinate system for the map is difficult without periodically calibrating the robot's position. The authors demonstrate that in a suitable environment it is possible to use sonar data to correct position and orientation estimates on an ongoing basis. This is accomplished by incrementally constructing and updating a model-based description of the acquired data. Given coarse position estimates of the robot's location and orientation, these can be refined to high accuracy using the stored map and a set of sonar readings from a single position. This approach is then generalized to allow global position estimation, where position and orientation estimates may not be available. The authors consider the accuracy of the method based on a single sonar reading and illustrate its region of convergence using empirical data.>
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".