An Integrated Robotic Multi-Modal Range Sensing System
Bibliographic record
Abstract
Creating 3-D surface representation of large objects or wide working areas is a tedious and error-prone process using the currently available sensor technologies. The primary problem comes from the fact that laser range sensors allow to capture at most one line of points from a given position and orientation, and stereo vision systems accuracy is dependent upon the initial camera calibration, the extraction of features, and the matching of features. When the registration process is not properly controlled, registration errors tend to significantly degrade the accuracy of measurements, which is revealed to be critical in telerobotic operations where occupancy models are built directly from these range measurements. The reliability of range measurements within a singular range sensor technique can drastically distort the registration process, especially within environments unsuitable for the system. Instead of utilizing a single range sensor, we adopt the use of a multi-modal system allowing diverse modes of range sensing techniques to complement each other in the hope that one system's strength could be used to compensate for another system's weakness. Using a mixture of active and passive range sensing techniques, both giving dense and sparse datasets, this multi-modal range sensing system is integrated seamlessly with minimal processing overhead and optimal 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".