Continuous motion, outdoor, 2 1/2D grid map generation using an inexpensive nodding 2-D laser rangefinder
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
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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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