A new Camera Model and Algorithms for higher Accuracy and better Convergence in Vision-based Pose Calculations

This paper presents novel strategies for better calibration and pose calculations of a system for determining the pose, i.e. position and orientation, of a camera. The system in question has a camera aimed to be placed on the hand of an industrial robot for welding, but is useful for any application with a need for measuring position and/or orientation. To calculate the pose of the camera circular reference points that can be recognized in the images are distributed in the working area. From their 2D image coordinates the 6D pose of the camera can be calculated. First the system is calibrated, i.e. the positions of the reference points and the camera parameters are determined. This is done by first taking images of the reference points from different locations, and then do a "total calibration" procedure to calculate the unknown parameters. For a specific system, called PosEye, it was concluded that the accuracy needs to be improved for welding applications. Also a method for making the calculations converge more easily, was needed. To meet these demands a new camera model is proposed, and three preprocessing calculation steps are presented. The new camera model increases accuracy by considering more distortion effects. The preprocessing steps give better initial values for more robust convergence of the algorithms and increased accuracy