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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".