Continuous motion, outdoor, 2 1/2D grid map generation using an inexpensive nodding 2-D laser rangefinder
Bibliographic record
Abstract
This paper introduces a technique for creating 2 1/2D grid maps of unstructured, outdoor environments, while traveling at high speeds, using an inexpensive nodding 2-D laser rangefinder. The nodding mechanism allows the acquisition of multiple range data sets for terrain in front of the robot. While these multiple data sets alleviate some of the problems traditionally associated with laser rangefinders, they also introduce a new set of problems. The paper investigates and quantifies factors that determine the accuracy of a map generated using a nodding laser rangefinder and derives an optimal basis for minimizing these errors. This research has determined that the most significant source of errors, for a nodding laser rangefinder configuration, are the roll, pitch and yaw accuracy for the laser beam. A variance weighted statistical approach was implemented to optimally fuse the range data into the 2 1/2D grid map. Simulations and experiments were conducted, demonstrating the performance of the variance weighted technique as superior to classical statistical methods
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".