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.

A suspension rail type greenhouse comprehensive information automatic cruise monitoring device, includes a sliding rail a sliding platform, and a lifting and lowering mechanism suspended on a greenhouse truss; a multi-sensor system which includes a binocular vision multifunctional camera, a laser ranging sensor, an infrared temperature measuring sensor, an illumination intensity sensor, and a temperature and humidity sensor, and an electronically controlled rotary pan-tilt mounted below the lifting and lowering mechanism of the sliding platform; a detection azimuth overlooks plant canopies; and a multi-sensor system configured to perform stationary point detection on the plant canopies one by one along planting lines of plants under the driving of the sliding platform.

In a method for the start-up operation of a multi-axis system, with the multi-axis system including, as components, segments connected via respective joints and are movable in one or more axes, and a tool, connected to one of the segments and is movable to a specified position, optical markers are arranged in the environment. Position coordinates of the optical markers in a first, global coordinate system are ascertained and stored in the controller. The environment is captured as image data by a camera system. The image data are transmitted to an AR system and visualized in an output apparatus. The optical markers and virtual markers are represented during the visualization of the image data, wherein a respective virtual marker is assigned to an optical marker. A check is performed as to whether an optical marker and the virtual marker overlay one another in the visualized image data.

A system is disclosed for providing extrinsic calibration of a camera to a relative working environment of a programmable motion device that includes an end-effector. The system includes a fiducial located at or near the end-effector, at least one camera system for viewing the fiducial as the programmable motion device moves in at least three degrees of freedom, and for capturing a plurality of images containing the fiducial, and a calibration system for analyzing the plurality of images to determine a fiducial location with respect to the camera to permit calibration of the camera with the programmable motion device.

A method for the start-up operation of a multi-axis system, the multi-axis system having segments which are movable by a controller in one or more axes, and a tool which is connected to one of the segments and is movable and drivable to a specified position by the controller. The method includes assigning a workspace and a safe space to the multi-axis system, arranging optical markers in an environment, making it possible for an augmented reality system to determine the position of a camera system which records the multi-axis system within the environment, defining a bounding body for each of the components such that the bounding body encloses the component, calculating a position of the bounding body during the movement of the multi-axis system, visualizing the bounding bodies together with an image recorded by the camera system, and checking whether the bounding body intersects with the safe space.

A calibration method for an optical see-through display includes the following. An image sensing device is fixed at a standard position of an eye of a user seeing the optical see-through display, and the image sensing device is configured to photograph a virtual image displayed by the optical see-through display and an image of a real object in an environment. Extrinsic parameters between the image sensing device and a virtual camera are calculated by using extrinsic parameters between the image sensing device and multiple calibration patterns of the real object as well as extrinsic parameters between the virtual camera and the multiple calibration patterns of the real object. A calibration system is also provided.

An apparatus and associated methodology providing a processor-controlled end effector that is selectively moveable according to end effector coordinates. A camera is positioned to detect objects according to camera coordinates that overlap the end effector coordinates. Logic executes computer instructions stored in memory to obtain a plurality of paired values of end effector coordinates and camera coordinates for each of a plurality of fiducial features, and to derive a transformation function from the plurality of paired values mapping the camera coordinates to the end effector coordinates.

An processor moves an arm to rotate a calibration pattern around three rotation axes linearly independent from each other and to stop at a plurality of rotation positions. A processor causes a camera to capture a pattern image of the calibration pattern at the plurality of rotation positions. A processor estimates parameters of the camera for calculating a coordinate transformation between a target coordinate system and a camera coordinate system using a pattern image captured at the plurality of rotation positions.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate an optical parameter related to an optical system imaging a target object, by using machine learning, detect the target object on the basis of an imaging result in the optical system by using the calculated optical parameter, and control a robot on the basis of a detection result of the target object.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate an operation parameter related to an operation of a robot by using machine learning, and control the robot on the basis of the calculated operation parameter.