An integrated robotic laser range sensing system for automatic mapping of wide workspaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Creating a 3D surface representation of large objects or wide working areas is a tedious and error-prone process using the currently available laser rangefinder technology. The primary problem comes from the fact that these range sensors are able to capture at most one line of points from a given position and orientation. When this process is not properly controlled, registration errors tend to degrade the measurement accuracy significantly; this is revealed to be critical in telerobotic operations where occupancy models are built directly from these range measurements. The paper presents the implementation of a prototype that has been developed to automatize the process of collecting range measurements by integrating a high-end one degree-of-freedom laser rangefinder with a seven degree-of-freedom serial robotic manipulator. The development of a user-friendly interface to control every part of the scanning process is also described, as it significantly improves performance and facilitates data processing and storage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it