Extrinisic Calibration of a Camera-Arm System Through Rotation Identification
This addresses the need for calibration in robotic systems outside lab environments, offering a method that avoids tedious measurements or external tools, though it appears incremental as it builds on known kinematics and conic observations.
The paper tackles the problem of extrinsic calibration for camera-arm systems without external artifacts by using structured arm motion and image plane conics to compute maximum-likelihood estimates, achieving results consistent with ruler-based estimates in simulation and real-world tests.
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 arm's end effector without requiring tedious measurements or external artifacts. Index Terms: robotics, hand-eye problem, self-calibration, structure from motion