An adaptive configuration-space and work-space based criterion for view planning
Bibliographic record
Abstract
We consider the view planning problem, also called, next best view (NBV) problem for sensor based exploration for general robot-sensor systems, where a range scanner is mounted on a robot with non-trivial kinematics, e.g., an eye-in-hand system. Earlier approaches to NBV had considered purely work-space (we also use the term physical-space) based criteria, such as select the view that maximizes the unknown physical-space volume. While this works well for mobile robots (often modeled as point or circle, thereby having trivial geometry and kinematics), it ignores a critical aspect, i.e., to give priority to exploring "manoeuvrable" space around the robot so that it can move to better viewing configurations. Proposed C-space based view planning criteria address this problem. However, C-space criteria (assuming the robot has enough manoeuvrable space) may sacrifice efficiency in work-space volume coverage. For inspection or environment modeling tasks, efficient workspace volume coverage is important. In this paper, we propose an adaptive algorithm that biases the search toward C-space or toward work-space, as needed. We call it adaptive viewpoint candidates entropy (VCE) criterion. Results with different simulated scenes show the effectiveness of this criterion in efficient (in that it needs less scans) exploration of the workspace.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".