A calibration device is provided. The calibration device includes a frame, a first optical sensing device, a second optical sensing device and a third optical sensing device. The frame includes a bottom plate and at least four sidewalls, wherein the sidewalls have a first grating hole, a second grating hole, a third grating hole and a fourth grating hole at a first height. The bottom plate has an image recognition pattern, a first measurement point, a second measurement point and a third measurement point.

A method calculates a calibration parameter for a robot tool. The method is based on the reception of an image dataset from medical imaging of an image volume via a first interface. The image volume contains a part of the robot tool and the robot tool is attached to a robot. A robot dataset is received by a second interface. The robot dataset contains a position of a movable axis of the robot during the recording of the image dataset. The position and/or orientation of a marking in the image dataset are determined by a computing unit. An image-based position and/or orientation of the tool center point of the robot tool are calculated by transforming the position and/or orientation of the marking. The calibration parameter is calculated based on the robot dataset and on the image-based position and/or orientation of the tool center point via the computing unit.

A calibration article is provided for calibrating a robot and 3D camera. The calibration article includes side surfaces that are angled inward toward a top surface. The robot and camera are calibrated by capturing positional data of the calibration article relative to the robot and the camera. The captured data is used to generate correlation data between the robot and the camera. The correlation data is used by the controller to align the robot with the camera during operational use of the robot and camera.

For calibration on a single camera or a stereo camera, a calibration range is set in advance in an image coordinate system and the calibration is performed in an arbitrary range. A visual sensor controller is a calibration device that associates a robot coordinate system at a robot and an image coordinate system at a camera by placing a target mark at the robot, moving the robot, and detecting the target mark at multiple points in a view of the camera. The calibration device comprises: an image range setting unit that sets an image range in the image coordinate system at the camera; and a calibration range measurement unit that measures an operation range for the robot corresponding to the image range before implementation of calibration by moving the robot and detecting the target mark.

A method is provided for automatically calibrating a camera-robot system having tool offsets.

A robot control system and a method for automatic camera calibration is presented. The robot control system includes a control circuit configured to control a robot arm to move a calibration pattern to at least one location within a camera field of view, and to receive a calibration image from a camera. The control circuit determines a first estimate of a first intrinsic camera parameter based on the calibration image. After the first estimate of the first intrinsic camera parameter is determined, the control circuit determines a first estimate of a second intrinsic camera parameter based on the first estimate of the first intrinsic camera parameter. These estimates are used to determine an estimate of a transformation function that describes a relationship between a camera coordinate system and a world coordinate system. The control circuit controls placement of the robot arm based on the estimate of the transformation function.

A robot control system and a method for automatic camera calibration is presented. The robot control system includes a control circuit configured to control a robot arm to move a calibration pattern to at least one location within a camera field of view, and to receive a calibration image from a camera. The control circuit determines a first estimate of a first intrinsic camera parameter based on the calibration image. After the first estimate of the first intrinsic camera parameter is determined, the control circuit determines a first estimate of a second intrinsic camera parameter based on the first estimate of the first intrinsic camera parameter. These estimates are used to determine an estimate of a transformation function that describes a relationship between a camera coordinate system and a world coordinate system. The control circuit controls placement of the robot arm based on the estimate of the transformation function.

A robot arm apparatus including: an arm unit made up of a plurality of links joined by one or a plurality of joint units, the arm unit is connectable to an imaging unit. An internal model including at least geometric information of the arm unit and focus position information of the imaging unit is updated based on internal model information acquired in a state in which the imaging unit is pointed at a reference point in a real space.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that automatically calibrates robots and sensors assigned to perform a task in an operating environment. One of the methods includes obtaining a representation of a robotic operating environment. A user selection of a plurality of components to be configured to operate in the robotic operating environment is received. A mapping is obtained between pairs of components to be calibrated and one or more respective calibration processes to perform to calibrate each pair of components. From the mapping, one or more calibration processes to be performed on pairs of components based on the user selection of the plurality of components is computed. Calibration instruction data describing how to perform the one or more calibration processes to be performed on the pairs of components of the user selection is determined and presented.

The present disclosure provides a method for simultaneously performing a robot's kinematic calibration and hand-eye calibration. One exemplary method comprises acquiring a plurality of point clouds of a calibration fixture and formulating this simultaneous calibration problem as an optimization problem based on the collection of point clouds of the same fixture.