A robot including an arm, a tool attached to the arm, a measurement fixture to be attached to a tip portion of the tool detachably, and a controller that recognizes a reference coordinate system used to control the arm, and that controls the arm. The controller stores data indicating a positional relationship between a tip of the tool and the measurement fixture or data to be used to calculate the positional relationship, and the controller calculates positional coordinates of the tip of the tool in the reference coordinate system based on position data of the measurement fixture and the positional relationship, where the position data is detected by using acquired image data from a visual sensor whose position is associated with the reference coordinate system.

A computing system and a method for calibration verification is presented. The computing system is configured to perform a first calibration operation, and to control a robot arm to move a verification symbol to a reference location. The robot control system further receives, from a camera, a reference image of the verification symbol, and determines a reference image coordinate for the verification symbol. The robot control system further controls the robot arm to move the verification symbol to the reference location again during an idle period, receives an additional image of the verification symbol, and determines a verification image coordinate. The robot control system determines a deviation parameter value based the reference image coordinate and the verification image coordinate, and whether the deviation parameter value exceeds a defined threshold, and performs a second calibration operation if the threshold is exceeded.

A system and method for performing automatic camera calibration is provided. The system receives a calibration image, and determines a plurality of image coordinates for representing respective locations at which a plurality of pattern elements of a calibration pattern appear in a calibration image. The system determines, based on the plurality of image coordinates and defined pattern element coordinates, an estimate for a first lens distortion parameter of a set of lens distortion parameters, wherein the estimate for the first lens distortion parameter is determined while estimating a second lens distortion parameter of the set of lens distortion parameters to be zero, or is determined without estimating the second lens distortion parameter. The system determines, after the estimate of the first lens distortion parameter is determined, an estimate for the second lens distortion parameter based on the estimate for the first lens distortion parameter.

One embodiment can provide a robotic system. The system can include a machine-vision module, a robotic arm comprising an end-effector, a robotic controller configured to control movements of the robotic arm, and an error-compensation module configured to compensate for pose errors of the robotic arm by determining a controller-desired pose corresponding to a camera-instructed pose of the end-effector such that, when the robotic controller controls the movements of the robotic arm based on the controller-desired pose, the end-effector achieves, as observed by the machine-vision module, the camera-instructed pose. The error-compensation module can include a machine learning model configured to output an error matrix that correlates the camera-instructed pose to the controller-desired pose.

A calibration method for a robotic arm system is provided. The method includes: capturing an image of a calibration object fixed to a front end of the robotic arm by a visual device, wherein a pedestal of the robotic arm has a pedestal coordinate system, and the front end of the robotic arm has a first relative relationship with the pedestal, the front end of the robotic arm has a second relative relationship with the calibration object; receiving the image and obtaining three-dimensional feature data of the calibration object according to the image by a computing device; and computing a third relative relationship between the visual device and the pedestal according to the three-dimensional feature data, the first relative relationship, and the second relative relationship to calibrate a position error between a physical location of the calibration object and a predictive positioning-location generated by the visual device.

A method, instructions for which are executed from a computer-readable medium, calibrates a robotic camera system having a digital camera connected to an end-effector of a serial robot. The end-effector and camera move within a robot motion coordinate frame (“robot frame”). The method includes acquiring, using the camera, a reference image of a target object on an image plane having an optical coordinate frame, and receiving input signals, including a depth measurement and joint position signals. Separate roll and pitch offsets are determined of a target point within the reference image with respect to the robot frame while moving the robot. Offsets are also determined with respect to x, y, and z axes of the robot frame while moving the robot through another motion sequence. The offsets are stored in a transformation matrix, which is used to control the robot during subsequent operation of the camera system.

According to one aspect of the present invention, disclosed is an automatic calibration method for a calibration device connected to a camera that is disposed the end effector of a robot and to a robot controller for controlling the robot. The method comprises the steps of: acquiring, from the camera and the robot controller, a robot-based coordinate system and an image of a marker marked in the work area of the robot (wherein the acquired image and robot-based coordinate system are recorded while the end effector is moved to a plurality of sample coordinates); and estimating the position of a robot coordinate system-based marker by using the acquired image and robot-based coordinate system.

The invention describes a generic framework for hand-eye calibration of camera-guided apparatuses, wherein the rigid 3D transformation between the apparatus and the camera must be determined. An example of such an apparatus is a camera-guided robot.

A system and method for performing automatic camera calibration is provided. The system receives a calibration image, and determines a plurality of image coordinates for representing respective locations at which a plurality of pattern elements of a calibration pattern appear in a calibration image. The system determines, based on the plurality of image coordinates and defined pattern element coordinates, an estimate for a first lens distortion parameter of a set of lens distortion parameters, wherein the estimate for the first lens distortion parameter is determined while estimating a second lens distortion parameter of the set of lens distortion parameters to be zero, or is determined without estimating the second lens distortion parameter. The system determines, after the estimate of the first lens distortion parameter is determined, an estimate for the second lens distortion parameter based on the estimate for the first lens distortion parameter.

Systems and methods of optical registration in a computer-aided system includes a control system. The control system is configured to provide a first registration indicator having a first indicator pose in a workspace; determine a first robotic device pose in a robotic device reference frame; obtain a first image of the first registration indicator; determine, based on the first image, the first indicator pose in an imaging device reference frame; provide a second registration indicator having a second indicator pose in the workspace; determine a second robotic device pose in the robotic device reference frame; obtain a second image of the second registration indicator; determine, based on the second image, the second indicator pose in the imaging device reference frame; and determine a registration transform between the robotic device reference frame and the imaging device reference frame based on correspondences between the robotic device poses and the indicator poses.