A New Formulation for Hand–Eye Calibrations as Point-Set Matching
Bibliographic record
Abstract
Conventional methods formulate the calibration problem between a robot hand and a camera, also known as hand-eye-calibration problem, as AX = XB. However, in practice, these methods have limited accuracy, as will be shown in our experimental results. In this article, we formulated the hand-eye calibration problem as a point-set-matching problem and proposed a new approach to solve this problem. The proposed approach is particularly suitable for robotic applications and offers good accuracy. We obtain a solution for the said problem using the gradient-descent (GD) technique on the special Euclidean group SE(3). We call this approach GD-SE(3). To prove the validity of the proposed approach and to demonstrate its advantages, experimental results are provided, where we compare the performance of GD-SE(3) with both conventional solutions for the hand-eye calibration problem as well as those based on point-set matching. The results show that the accuracy of GD-SE(3) is comparable with those based on other point-set-matching algorithms. Yet, it outperforms conventional formulations based on AX = XB while offering a more suitable approach for the hand-eye calibration problem of robot manipulators.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".