A calibration device for an imaging device includes an imaging data acquisition unit that acquires imaging data of a known external target installed at a known position outside a work range of work equipment, the imaging data being obtained by imaging of at least one imaging device provided in a work machine including the work equipment, an external target position acquisition unit that acquires a position of the known external target, and a calibration unit that calibrates the imaging device based on the position of the known external target, which is acquired by the external target position acquisition unit, and the imaging data of the known external target, which is acquired by the imaging data acquisition unit.

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.

An augmented reality visualizer and measurement system for swimming pool components (e.g., liners and safety covers) is provided in some examples of the present disclosure. In one example, a system can receive images of physical markers positioned spatially around a perimeter of a swimming pool in real space. The system can identify the physical markers in the images by analyzing the images using one or more image processing techniques. The system can determine one or more characteristics of the swimming pool based on relationships between the physical markers. The system can then generate pool component information based on the determined one or more characteristics of the swimming pool, and output the pool component information.

A method of training a lifeguard to properly view an area of a swimming pool or body of water and recognize a swimmer/bather in distress. The method includes: positioning submersible devices or other objects on a bottom of the swimming pool or body of water according to an established grid or pattern; observing the submersible devices to make observations; analyzing the observations to evaluate the ability to see the submersible devices under varying environmental and density conditions. The observation trains the lifeguard to recognize the swimmer/bather in distress in the swimming pool or body of water to minimize the risk of the swimmer/bather drowning.

A relative position calculator calculates relative position and attitude of a vehicle based on images captured by an image capturing apparatus on the vehicle. An absolute position calculator extracts a marker from an image captured by the image capturing apparatus, and calculates absolute position and attitude of the vehicle, based on position and attitude of the one extracted marker. A corrector calculates the corrected position and attitude, not using the absolute position and attitude calculated based on the position and attitude of the marker when a difference or ratio of an apparent height and width of the marker in the image is equal to or smaller than a threshold, but using the absolute position and attitude calculated based on the position and attitude of the marker when the difference or ratio is larger than the threshold.

Systems and methods include obtaining point cloud data representing a point cloud; selecting a subset of the point cloud data based at least in part on a contour metric; grouping sets of points of the subset of the point cloud into one or more clusters based at least in part on one or more distance metrics; for a cluster that satisfies one or more cluster size criteria based on dimensions of a calibration standard, determining whether a distribution of signal intensities of points of the cluster satisfies a distribution criterion; based on a determination that the distribution of signal intensities of points satisfies the distribution criterion, determining boundaries of a region that represents the calibration standard; and storing data identifying a set of points of the point cloud that correspond to the calibration standard.

A method for creating a camera model for a camera of a surgical microscope includes positioning a calibration object in an initial pose in an observation region of the camera, determining a pose delta for reaching a first pose for the calibration object in a measurement space of the camera starting from the initial pose, positioning the calibration object in the first pose in accordance with the determined pose delta, making a recording of the calibration object in the first pose with the camera, positioning the calibration object in at least one further pose, making a recording of the calibration object in the at least one further pose, and creating a camera model based on the recordings made, the first pose and the at least one further pose being chosen with a distribution in the measurement space such that a camera model is obtained which represents the entire measurement space.

A positioning device combining mark point positioning and intelligent reverse positioning and method thereof, comprising a binocular camera, a third camera, and a laser; the laser is used for emitting laser projection, the binocular camera is used for acquiring images with laser lines and reflective mark points on the surface of the scanned object, and the third camera is used for acquiring images with coding points and mark points in the peripheral environment; the method comprises the following steps of: S1. calibrating parameters of each camera under different scanning modes, and enabling the parameters of each camera to synchronously and correspondingly transform when the scanning modes are switched; S2. judging and switching the scanning mode into a mark point mode or an intelligent reverse tracking mode through the scanning scene. The two positioning modes are flexibly switched, and the use of a user is facilitated.

The autonomous driving device including a communication circuit configured to communicate with an unmanned aerial vehicle, a plurality of sensors disposed in the autonomous vehicle to monitor all directions of the autonomous vehicle, and a processor, wherein the processor is configured to: control the unmanned aerial vehicle to hover at each of a plurality of waypoints of a designated flight path by controlling a relative position of the unmanned aerial vehicle through the communication circuit, change a posture angle of the unmanned aerial vehicle to a plurality of posture angles corresponding to the waypoints of the flight path, generate a plurality of images including the checkerboard and corresponding to the plurality of waypoints and the plurality of posture angles through the plurality of sensors, and calibrate the plurality of sensors on the basis of a relationship between matching points of the plurality of images.

An apparatus includes an interface and a processor. The interface may be configured to receive pixel data representing respective fields of view of two or more cameras arranged to obtain a predetermined field of view, where the respective fields of view of each adjacent pair of the two or more cameras overlap. The processor may be configured to process the pixel data arranged as video frames and perform a fixed pattern calibration for facilitating multi-view stitching. The fixed pattern calibration may comprise applying a pose calibration process to the video frames. The pose calibration process generally uses (i) intrinsic parameters, a respective translate vector, a respective rotation matrix, and distortion parameters for each lens of the two or more cameras and (ii) a calibration board to obtain configuration parameters for the respective fields of view of the two or more cameras. The pose calibration process may comprise changing a z value of the respective translate vector for each lens of the two or more cameras to at least one of a middle distance value and a long distance value while maintaining the respective rotation matrix for each lens of the two or more cameras unchanged.