Name
Abstract | ||
|---|---|---|
Determining extrinsic calibration parameters is a necessity in any robotic system composed of actuators and cameras. Once a system is outside the lab environment, parameters must be determined without relying on outside artifacts such as calibration targets. We propose a method that relies on structured motion of an observed arm to recover extrinsic calibration parameters. Our method combines known arm kinematics with observations of conics in the image plane to calculate maximum-likelihood estimates for calibration extrinsics. This method is validated in simulation and tested against a real-world model, yielding results consistent with ruler-based estimates. Our method shows promise for estimating the pose of a camera relative to an articulated armu0027s end effector without requiring tedious measurements or external artifacts. Index Terms: robotics, hand-eye problem, self-calibration, structure from motion |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Robotics | Journal |
Volume | Citations | PageRank |
abs/1812.08280 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steve McGuire | 1 | 4 | 1.78 |
| Christoffer R. Heckman | 2 | 12 | 10.78 |
| Daniel Szafir | 3 | 230 | 23.05 |
| Simon Justin Julier | 4 | 38 | 10.49 |
| Nisar R. Ahmed | 5 | 2 | 2.41 |