Calibration of an Integrated Robotic Multimodal Range Scanner
Bibliographic record
Abstract
Collecting dense range measurements in uncontrolled environments is a challenging problem, as the quality of the measurements is highly dependent on the lighting conditions and the texture of the target surfaces. This dependence affects the registration and data-fusion processes and, consequently, degrades the accuracy of the surface or occupancy models that are computed from the range measurements. Typical approaches to address this issue have concentrated on improving a specific type of range sensor. On the other hand, the overall quality of the sensing can also be enhanced through the development of a mechanism that combines the various range-sensing technologies to form a multimodal range sensor. The resulting problem of the merging datasets can then be solved in two ways: system calibration of the multimodal sensor or data fitting of all the datasets into a single model, of which the latter is more widely implemented. The lack of multimodal-system calibration approaches is due to their complicated and lengthy nature, where individual calibration procedures must be applied to each subsystem and then applied between the subsystems of the multimodal range sensor. This paper proposes a technique to alleviate the problems encountered in a multimodal-system calibration. Straightforward and generic guidelines for the calibration are defined and applied to an in-house integrated multimodal system built from a laser-range-finder system, two structured-lighting systems, and a stereovision system. The system's intracalibration and intercalibration processes are detailed. Reconstructed renderings of the datasets collected with the calibrated multimodal range sensor, without the use of data fitting, are also presented. From these results, the potential benefits of multimodal calibration over the computationally intensive data-fitting methods and the advantages of merging the subsystem's strengths to complement other subsystem's weaknesses are put in evidence
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".