Extrinisic Calibration of a Camera-Arm System Through Rotation Identification

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 motio

Locating Camera Position in 3-D Space from Distinct Features of Architecture on 2-D Image

This research aimed to develop an algorithm that estimates the camera position in space from which an image was created using computer vision techniques. The implemented algorithm involves 3 major steps: defining a distinct combination of features of the object, recognizing the object with the distinct features, and calculating camera position using the mapping information between the projected 2-D image and the 3-D object. A generalized approach and a specific case study of the Cathedral of Notre Dame in Paris are discussed in detail