Integrated view and path planning for an autonomous six-DOF eye-in-hand object modeling system
Bibliographic record
Abstract
We present an integrated and fully autonomous eye-in-hand system for 3D object modeling. The system hardware consists of a laser range sensor mounted on a six-DOF manipulator arm and the task is to autonomously build 3D model of an object in-situ, i.e., the object may not be moved and must be scanned in its original location. Our system assumes no knowledge of either the object or the rest of the workspace of the robot. The overall planner integrates a next best view (NBV) algorithm along with a sensor-based roadmap planner. Our NBV algorithm while considering the key constraints such as FOV, viewing angle, overlap and occlusion, efficiently searches the five-dimensional view space to determine the best modeling view configuration. The sensor-based roadmap planner determines a collision-free path, to move the manipulator so that the wrist mounted scanner is at the view configuration. If the desired view configurations are not collision free, or there is no free path to reach them, the planner explores the workspace such that facilitates the modeling. This is repeated until the entire object is scanned. We have implemented the system and our results show that system is able to autonomously build a 3D model of an object in an unknown environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".