Camera Calibration Errors Research Articles

Relative spatial pose estimation for autonomous grasping

A technique for finding the relative spatial pose between a robotic end effector and a target object to be grasped without a priori knowledge of the spatial relationship between the camera and the robot is presented. The transformation between the coordinate system of the camera and the coordinate system of the robot is computed dynamically using knowledge about the location of the end effector relative to both the camera and the robot. A previously developed computer vision tech- nique is used to determine the pose of the end effector relative to the camera. The robot geometry and data from the robot controller is used to determine the pose of the end effector relative to the robot. The spatial transformation between the robot end effector and the target object is computed with respect to the robot's coordinate system. The algorithm was demonstrated using a five-degree-of-freedom robot and an RGB camera system. The camera can be dynamically positioned without con- cern for an assumed spatial relationship between the camera and robot, enabling optimization of the view of the object and the end effector. Further, the iterative nature of the grasping algorithm reduces the effects of camera calibration errors. © 1997 Society of Photo-Optical Instrumentation En- gineers. (S0091-3286(97)01612-7)

Object Pose: The Link between Weak Perspective,Paraperspective, and Full Perspective

Recently, DeMenthon and Davis (1992, 1995) proposed a method for determining the pose of a 3-D object with respect to a camera from 3-D to 2-D point correspondences. The method consists of iteratively improving the pose computed with a weak perspective camera model to converge, at the limit, to a pose estimation computed with a perspective camera model. In this paper we give an algebraic derivation of DeMenthon and Davis‘ method and we show that it belongs to a larger class of methods where the perspective camera model is approximated either at zero order (weak perspective) or first order (paraperspective). We describe in detail an iterative paraperspective pose computation method for both non coplanar and coplanar object points. We analyse the convergence of these methods and we conclude that the iterative paraperspective method (proposed in this paper) has better convergence properties than the iterative weak perspective method. We introduce a simple way of taking into account the orthogonality constraint associated with the rotation matrix. We analyse the sensitivity to camera calibration errors and we define the optimal experimental setup with respect to imprecise camera calibration. We compare the results obtained with this method and with a non-linear optimization method.

A modular system for robust positioning using feedback from stereo vision

This paper introduces a modular framework for robot motion control using stereo vision. The approach is based on a small number of generic motion control operations referred to as primitive skills. Each primitive skill uses visual feedback to enforce a specific task-space kinematic constraint between a robot end-effector and a set of target features. By observing both the end-effector and target features, primitive skills are able to position with an accuracy that is independent of errors in hand-eye calibration. Furthermore, primitive skills are easily combined to form more complex kinematic constraints as required by different applications. These control laws have been integrated into a system that performs tracking and control on a single processor at real-time rates. Experiments with this system have shown that it is extremely accurate, and that it is insensitive to camera calibration error. The system has been applied to a number of example problems, showing that modular, high precision, vision-based motion control is easily achieved with off-the-shelf hardware.