Experimental determination of relative motion measurement accuracy for an auto-synchronous triangulation scanning laser camera
Bibliographic record
Abstract
The navigation of an autonomous robotic vehicle is a difficult task. Accurate measurement of robotic vehicle motion is a problem in certain environments. In desert and other terrains, wheel slip affects the accuracy of odometry sensors. Poorly-lit underground environments present problems for passive vision systems. As well, for slow-moving vehicles, the effects of INS drift errors can be large even over short distances. An active triangulation scanning laser camera sensor, which can provide accurate 3D images at distances less than 10m, has the potential to alleviate the problems mentioned above by improving the accuracy of integrated navigation systems for robotic vehicles operating in such environments. Knowledge of the relative position measurement accuracy for scanning laser cameras in various environments will allow navigation system designers to determine whether incorporating these sensors will help to meet their system accuracy requirements. This paper presents an experimental method for determining relative position measurement accuracy of an auto-synchronous triangulation scanning laser camera. 3D images were taken of a simulated desert terrain environment from multiple camera positions and orientations. Registration of overlapping images using an Iterative Closest Point (ICP) based algorithm was performed to determine an estimate of the position and orientation change of the laser camera. Truth data for the position and orientation of the laser camera at each location was determined by using theodolites to measure the location of survey targets mounted on the laser camera. The relative position estimates were then compared to the truth data. In this paper, the experiment design and implementation are detailed, and preliminary experimental results are presented.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".