Map building for a terrain scanning robot
Bibliographic record
Abstract
Presents the application of an image registration method for a mobile manipulator. The robot is used for scanning natural terrain and detecting metal objects hidden beneath the terrain surface (e.g., landmines) using a metal detector. The range image may be interpreted for visual servoing, map building and path planning, or object recognition. In the work, the image is used to build a terrain map for obstacle free path planning. Because the working area of the robot is extremely dynamic (i.e., not only the robot travels but also the environment is also subject to change) an active range sensing method is selected to provide the range image. The range values are acquired using a laser range finder with a rotating mirror for scanning so that sensor fusion in the form of collecting sensor readings over an extended period of time is required. In addition, range readings of two ultrasonic range finders are fused at signal level to tackle both sensor imperfection and environmental illumination that induce uncertainty at the system. We explain the use of a real-time programming platform that executes an online map-building process in parallel for robot manipulation and control.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".