A Method for Hand-Eye and Camera-to-Camera Calibration for Limited Fields of View

Abstract

In classical robot-camera calibration, a 6D transformation between the camera frame and the local frame of a robot is estimated by first observing a known calibration object from a number of different view points and then finding transformation parameters that minimize the reprojection error. The disadvantage with this is that often not all configurations can be reached by the end-effector, which leads to an inaccurate parameter estimation. Therefore, we propose a more versatile method based on the detection of oriented visual features, in our case AprilTags. From a collected number of such detections during a defined rotation of a joint, we fit a Bingham distribution by maximizing the observation likelihood of the detected orientations. After a tilt and a second rotation, a camera-to-joint transformation can be determined. In experiments with accurate ground truth available, we evaluate our approach in terms of precision and robustness, both for hand-eye/robot-camera and for camera- camera calibration, with classical solutions serving as a baseline.