Simultaneous Hand–Eye/Robot–World/Camera–IMU Calibration
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
The problem of calibrating an extrinsic parameter between a camera and an inertial measurement unit (IMU) using an industrial robotic manipulator has been studied. This generates a result of hand–eye/robot–world/camera–IMU calibration in a simultaneous fashion. The developed method is free of inertial integration over time and, thus, is robust to uncertain IMU biases. It is derived that the problem can be solved via a simultaneous optimization of hand–eye/robot–world/camera–IMU transformations. The resulted optimization is highly nonconvex on the special Euclidean group, and we give globally optimal solutions. Experiments verify that the proposed method is capable of estimating accurate calibration parameters. Comparative studies between representatives show the global optimality of the proposed method. The new simultaneous method is capable of conducting calibration of a robot/camera/IMU combination. The designed method guarantees the global optimality; thus, the accuracy is ensured. The developed globally optimal solutions will also be computationally efficient on modern industrial computers. Finally, we show that the proposed method can give accurate calibration results for a stereo/IMU sensor combination.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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