Cognex Corporation

Guruprasad Shivaram

Cyril C Marrion

David Y Li

Xiangyun Ye

Gang Liu

Lifeng Liu

Aaron S Wallack

Tuotuo Li

David J Michael

Lowell D Jacobson

Willard P Foster

Nigel John Foster

This invention provides a system and method for runtime determination (self-diagnosis) of camera miscalibration (accuracy), typically related to camera extrinsics, based on historical statistics of runtime alignment scores for objects acquired in the scene, which are defined based on matching of observed and expected image data of trained object models. This arrangement avoids a need to cease runtime operation of the vision system and/or stop the production line that is served by the vision system to diagnose if the system's camera(s) remain calibrated. Under the assumption that objects or features inspected by the vision system over time are substantially the same, the vision system accumulates statistics of part alignment results and stores intermediate results to be used as indicator of current system accuracy. For multi-camera vision systems, cross validation is illustratively employed to identify individual problematic cameras. The system and method allows for faster, less-expensive and more-straightforward diagnosis of vision system failures related to deteriorating camera calibration.

System and method for runtime determination of camera miscalibration

This invention provides a system and method for generating camera calibrations for a vision system camera along three discrete planes in a 3D volume space that uses at least two (e.g. parallel) object planes at different known heights. For any third (e.g. parallel) plane of a specified height, the system and method then automatically generates calibration data for the camera by interpolating/extrapolating from the first two calibrations. This alleviates the need to set the calibration object at more than two heights, speeding the calibration process and simplifying the user's calibration setup, and also allowing interpolation/extrapolation to heights that are space-constrained, and not readily accessible by a calibration object. The calibration plate can be calibrated at each height using a full 2D hand-eye calibration, or using a hand-eye calibration at the first height and then at a second height with translation to a known position along the height (e.g. Z) direction.

System and method for calibration of machine vision cameras along at least three discrete planes

A vision-based manipulations system can be configured for, and can be operated with methods including, performing hand-eye calibrations at multiple workstations of the system, performing a cross-station calibration for the system, and determining relationships between the hand-eye calibrations and the cross-station calibration. In some embodiments, the system can be used to move a work object between the workstations based on the cross-station calibration.

Calibration and operation of vision-based manipulation systems

This invention provides a system and method that ties the coordinate spaces at the two locations together during calibration time using features on a runtime workpiece instead of a calibration target. Three possible scenarios are contemplated: wherein the same workpiece features are imaged and identified at both locations; wherein the imaged features of the runtime workpiece differ at each location (with a CAD or measured workpiece rendition available); and wherein the first location containing a motion stage has been calibrated to the motion stage using hand-eye calibration and the second location is hand-eye calibrated to the same motion stage by transferring the runtime part back and forth between locations. Illustratively, the quality of the first two techniques can be improved by running multiple runtime workpieces each with a different pose, extracting and accumulating such features at each location; and then using the accumulated features to tie the two coordinate spaces.

System and method for tying together machine vision coordinate spaces in a guided assembly environment

This invention provides a system and method for guiding the workpieces to optimal positions to train an assembly system that is generally free of the use of a CMM or similar metrology device. The system and method expresses the image features of the workpieces, when they are in their respective stations, in a common coordinate system. This ability allows a user to visualize the result of assembling the workpieces without actually assembling them, in a 'virtual assembly'. The virtual assembly assists guiding placement of workpieces in respective stations into a desired relative alignment. The system and method illustratively generates a composite image using the images from cameras used in guiding the workpieces that helps the user visualize how the part would appear following assembly. The user can reposition the images of workpieces in their respective stations until the composite image has a desired appearance.

Vision system for training an assembly system through virtual assembly of objects

This invention provides a system and method to validate the accuracy of camera calibration in a single or multiple-camera embodiment, utilizing either 2D cameras or 3D imaging sensors. It relies upon an initial calibration process that generates and stores camera calibration parameters and residual statistics based upon images of a first calibration object. A subsequent validation process (a) acquires images of the first calibration object or a second calibration object having a known pattern and dimensions; (b) extracts features of the images of the first calibration object or the second calibration object; (c) predicts positions expected of features of the first calibration object or the second calibration object using the camera calibration parameters; and (d) computes a set of discrepancies between positions of the extracted features and the predicted positions of the features. The validation process then uses the computed set of discrepancies in a decision process that determines whether at least one of the discrepancies exceeds a predetermined threshold value. If so, recalibration is required. Where multiple cameras are employed, extrinsic parameters are determined and used in conjunction with the intrinsics.

System and method for validating camera calibration in a vision system

This invention provides a system and method for determining the pose of shapes that are known to a vision system that undergo both affine transformation and deformation. The object image with fiducial is acquired. The fiducial has affine parameters, including degrees of freedom (DOFs), search ranges and search step sizes, and control points with associated DOFs and step sizes. Each 2D affine parameter's search range and the distortion control points' DOFs are sampled and all combinations are obtained. The coarsely specified fiducial is transformed for each combination and a match metric is computed for the transformed fiducial, generating a score surface. Peaks are computed on this surface, as potential candidates, which are refined until a match metric is maximized. The refined representation exceeding a predetermined score is returned as potential shapes in the scene. Alternately the candidate with the best score can be used as a training fiducial.

System and method for automatically determining pose of a shape

This invention provides a system and method for determining the three-dimensional alignment of a modeled object or scene. A 3D (stereo) sensor system views the object to derive a runtime 3D representation of the scene containing the object. Rectified images from each stereo head are preprocessed to enhance their edge features. 3D points are computed for each pair of cameras to derive a 3D point cloud. The amount of 3D data from the point cloud is reduced by extracting higher-level geometric shapes (HLGS), such as line segments. Found HLGS from runtime are corresponded to HLGS on the model to produce candidate 3D poses. A coarse scoring process prunes the number of poses. The remaining candidate poses are then subjected to a further more-refined scoring process. These surviving candidate poses are then verified whereby the closest match is the best refined three-dimensional pose.

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Wellesley, MA, United States of America

Guruprasad Shivaram