Cognex Corporation

Andrew Hoelscher

Nathaniel R Bogan

David J Michael

Adam Wagman

Zihan Hans Liu

Eric Moore

Benjamin Braker

Simon Barker

Hongjun Jia

Aaron S Wallack

The techniques described herein relate to methods, apparatus, and computer readable media configured to identify a surface feature of a portion of a three-dimensional (3D) point cloud. Data indicative of a path along a 3D point cloud is received, wherein the 3D point cloud comprises a plurality of 3D data points. A plurality of lists of 3D data points are generated, wherein: each list of 3D data points extends across the 3D point cloud at a location that intersects the received path; and each list of 3D data points intersects the received path at different locations. A characteristic associated with a surface feature is identified in at least some of the plurality of lists of 3D data points. The identified characteristics are grouped based on one or more properties of the identified characteristics. The surface feature is identified based on the grouped characteristics.

Methods and apparatus for identifying surface features in three-dimensional images

This invention applies dynamic weighting between a point-to-plane and point-to-edge metric on a per-edge basis in an acquired image using a vision system. This allows an applied ICP technique to be significantly more robust to a variety of object geometries and/or occlusions. A system and method herein provides an energy function that is minimized to generate candidate 3D poses for use in alignment of runtime 3D image data of an object with model 3D image data. Since normals are much more accurate than edges, the use of normal is desirable when possible. However, in some use cases, such as a plane, edges provide information in relative directions the normals do not. Hence the system and method defines a 'normal information matrix', which represents the directions in which sufficient information is present. Performing (e.g.) a principal component analysis (PCA) on this matrix provides a basis for the available information.

System and method for simultaneous consideration of edges and normals in image features by a vision system

The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a three-dimensional model. A three-dimensional model is stored, the three dimensional model comprising a set of probes. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted into a set of fields, comprising generating a first field comprising a first set of values, where each value of the first set of values is indicative of a first characteristic of an associated one or more data entries from the set of data entries, and generating a second field comprising a second set of values, where each second value of the second set of values is indicative of a second characteristic of an associated one or more data entries from the set of data entries, wherein the second characteristic is different than the first characteristic. A pose of the three-dimensional model is tested with the set of fields, comprising testing the set of probes to the set of fields, to determine a score for the pose.

Methods and apparatus for testing multiple fields for machine vision

Methods, systems, and devices for 3D measurement and/or pattern generation are provided in accordance with various embodiments. Some embodiments include a method of pattern projection that may include projecting one or more patterns. Each pattern from the one or more patterns may include an arrangement of three or more symbols that are arranged such that for each symbol in the arrangement, a degree of similarity between said symbol and a most proximal of the remaining symbols in the arrangement is less than a degree of similarity between said symbol and a most distal of the remaining symbols in the arrangement. Some embodiments further include: illuminating an object using the one or more projected patterns; collecting one or more images of the illuminated object; and/or computing one or more 3D locations of the illuminated object based on the one or more projected patterns and the one or more collected images.

Coding distance topologies for structured light patterns for 3D reconstruction

The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a model in three-dimensional data. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted to a field comprising a set of cells that each have an associated value, comprising determining, for each cell value, representative data based on one or more data entries from the set of data entries of the three-dimensional data. A pose of the model is tested with the field to determine a score for the pose.

Methods and apparatus for generating a dense field of three dimensional data for machine vision

This invention provides a system and method for estimating match of a 3D alignment pose of a runtime 3D point cloud relative to a trained model 3D point cloud. It includes scoring a match of a candidate pose of the runtime 3D point cloud relative to the trained model 3D point cloud, including a visibility check that comprises (a) receiving a 3D camera optical center (b) receiving the trained model 3D point cloud; (c) receiving the runtime 3D point cloud; and (d) constructing a plurality of line segments from the optical center to a plurality of 3D points in the 3D point cloud at the runtime candidate pose. A system and method for determining an accurate representation of a 3D imaged object by omitting spurious points from a composite point cloud based on the presence or absence of such points in a given number of point clouds is also provided.

System and method for improved scoring of 3D poses and spurious point removal in 3D image data

This invention provides a system and method for aligning first three-dimensional (3D) point cloud image representing a model with a second 3D point cloud image representing a target, using a vision system processor. A passing overall score is established for possible alignments of the first 3D point cloud image with the second 3D point cloud image. A coverage score for at least one alignment of the first 3D point cloud image with the second 3D point cloud image is estimated so that the coverage score describes an amount of desired features in the first 3D point cloud image present in the second 3D point cloud image. A clutter score is estimated so that the clutter score describes extraneous features in the second 3D point cloud image. An overall score is computed as a difference between the coverage score and the clutter score.

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Somerville, MA, United States of America

Andrew Hoelscher