Spatial occupancy recovery from a multiple-view range imaging system for path planning applications
Bibliographic record
Abstract
We strive to obtain full spatial occupancy descriptions of the workspace from multiple range images acquired from different points of view. Such representations are highly suitable in path-planning applications, for charting a collision-free course through a workspace. We propose an algorithm, called peeling, which allows us to fuse range images into a voxel array and obtain a spatial occupancy model for use by a path planner. We also take a look at another method of range image fusion, called direct mapping, which combines the images into a voxel array but does not provide a proper spatial occupancy model. We present an overview of previous work in range image fusion and look at octrees and octree generation; our final model is an octree representation, for more efficient memory use. We are building a prototype system consisting of a technical Arts 100 AT laser range scanner mounted on a PUMA 560 robot arm, both controlled from an SGI workstation. Spatial occupancy models generated by this system are fed to an off-line path-planner.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".