Systems and methods for generating an object recognition template
A computing system including a processing circuit in communication with a camera having a field of view. The processing circuit is configured to perform operations related to detecting, identifying, and retrieving objects disposed amongst a plurality of objects. The processing circuit may be configured to perform operations related to object recognition template generation, feature generation, hypothesis generation, hypothesis refinement, and hypothesis validation.
1. A computing system configured to generate an object recognition template for identifying an object in a scene comprising: at least one processing circuit configured for: obtaining object information including a digitally represented object; extracting two-dimensional measurement information from the object information, including extracting gradient information from the object information, the gradient information being indicative of a direction or orientation of a candidate edge of the digitally represented object; extracting three-dimensional measurement information from the object information, including extracting surface normal vector information from the object information, the surface normal vector information describing a plurality of vectors normal to a surface of the digitally represented object; and generating an object recognition template according to the two-dimensional measurement information and the three-dimensional measurement information, wherein the object recognition template is for an object picking operation for the object in the scene by a robot interaction.
2. The computing system of claim 1 , wherein the digitally represented object is an object model and extracting the two-dimensional measurement information and the three-dimensional measurement information is performed to generate feature maps of the object model at a selected viewpoint.
3. The computing system of claim 1 , wherein the at least one processing circuit is further configured for:
obtaining image information of the scene;
accessing the object recognition template; and
comparing the two-dimensional measurement information and the three-dimensional measurement information to the image information to identify the object as corresponding to the digitally represented object.
4. The computing system of claim 1 , wherein the object information includes registration data of the object and the digitally represented object includes an object model.
5. The computing system of claim 1 , wherein the object information includes at least one of two-dimensional image information and three-dimensional image information.
6. The computing system of claim 1 , wherein the gradient information is extracted at a plurality of gradient extraction locations of the digitally represented object, and extracting the gradient information includes analyzing pixel intensity of two-dimensional image information of the object information to measure a direction in which the pixel intensity of the two-dimensional image information at each gradient extraction location is changing.
7. The computing system of claim 1 , wherein the surface normal vector information is extracted at a plurality of surface normal locations of the digitally represented object, and extracting the surface normal vector information includes identifying the plurality of vectors normal to the surface of the digitally represented object at each surface normal location.
8. The computing system of claim 1 , wherein the gradient information is extracted at a plurality of gradient extraction locations of the digitally represented object, the surface normal vector information is extracted at a plurality of surface normal locations of the digitally represented object, and the plurality of gradient extraction locations are different than the plurality of surface normal locations.
9. The computing system of claim 8 , wherein the plurality of gradient extraction locations do not overlap with the plurality of surface normal locations.
10. The computing system of claim 8 , wherein the plurality of gradient extraction locations are disposed at edges of the digitally represented object and the plurality of surface normal locations are disposed away from the edges of the digitally represented object.
11. The computing system of claim 1 , wherein the object recognition template is configured for use by the computing system to identify the object in the scene.
12. A method of generating an object recognition template for identifying an object in a scene comprising: obtaining object information including a digitally represented object; extracting two-dimensional measurement information from the object information, including extracting gradient information from the object information, the gradient information being indicative of a direction or orientation of a candidate edge of the digitally represented object; extracting three-dimensional measurement information from the object information, including extracting surface normal vector information from the object information, the surface normal vector information describing a plurality of vectors normal to a surface of the digitally represented object; and generating an object recognition template according to the two-dimensional measurement information and the three-dimensional measurement information, wherein the object recognition template is for an object picking operation for the object in the scene by a robot interaction.
13. The method of claim 12 , further including:
generating feature maps of an object model at a selected viewpoint.
14. The method of claim 12 , further including:
obtaining image information of the scene;
accessing the object recognition template; and
comparing the two-dimensional measurement information and the three-dimensional measurement information to the image information to identify the object as corresponding to the digitally represented object.
15. The method of claim 12 , further including: extracting the gradient information at a plurality of gradient extraction locations of the digitally represented object;
and analyzing pixel intensity of two-dimensional image information of the object information to measure a direction in which the pixel intensity of the two-dimensional image-information at each gradient extraction location is changing.
16. The method of claim 12 , further including:
extracting the surface normal vector information at a plurality of surface normal locations of the digitally represented object; and
identifying the plurality of vectors normal to the surface of the digitally represented object at each surface normal location.
17. A non-transitory computer readable medium, configured with executable instructions for implementing a method for generating an object recognition template for identifying an object in a scene, operable by at least one processing circuit via a communication interface configured to communicate with a robotic system, the method comprising: receiving object information including a digitally represented object; performing an operation to extract two-dimensional measurement information from the object information, including extracting gradient information from the object information, the gradient information being indicative of a direction or orientation of a candidate edge of the digitally represented object; performing an operation to extract three-dimensional measurement information from the object information, including extracting surface normal vector information from the object information, the surface normal vector information describing a plurality of vectors normal to a surface of the digitally represented object; and outputting an object recognition template to the robotic system according to the two-dimensional measurement information and the three-dimensional measurement information, wherein the object recognition template is for an object picking operation for the object in the scene by a robot interaction.
18. The method of claim 17 , further including:
receiving image information of the scene;
accessing the object recognition template; and
outputting a comparison between the two-dimensional measurement information and the three-dimensional measurement information to the image information to the robotic system to identify the object as corresponding to the digitally represented object.