A method to segment a 3D surface point cloud for selective sensing in robotic exploration
Bibliographic record
Abstract
Autonomous robotic exploration in a 3D environment requires the acquisition of 3D data to create a consistent internal model of the environment from which objects can be recognized for the robot to interact with. As the acquisition of 3D data with stereo vision or a laser range finder can be a relatively long process, selective sensing is desired to optimize the amount of data collected to accurately represent the environment in a minimal amount of time. In order to perform selective sensing, a coarse acquisition of the environment first needs to take place. Regions of interest, such as edges and other boundaries, can then be identified so that an acquisition with higher spatial resolution can occur over bounded regions. For that purpose a segmentation method of the coarse data is proposed so that regions can be efficiently distinguished from each other. The method takes a raw 3D surface profile point cloud of varying point densities, organizes it into a mesh, and then segments the surfaces present in this point cloud, producing a segmented mesh, as well as an octree of labeled voxels corresponding to the segmentation. This mesh and octree may then be used for sensory selection to drive a robot exploration task. The method is demonstrated on actual datasets collected in a laboratory 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".