Abstract: Briefly described, one embodiment determines pose of an object of interest at a run time by capturing a first image of a first structured light pattern projected onto a first local surface of the object of interest; determining a first run-time data set from the captured first image, wherein the first run-time data set corresponds to information determined from the first structured light pattern projected onto the first local surface; comparing the determined first run-time data set and a corresponding first reference data set, the first reference data set corresponding to an ideal pose of the first local surface on an ideally posed reference object; and determining at least one first degree of constraint that defines a first partial pose of the first local surface, the at least one first degree of constraint based upon the comparison of the first run-time data set with the corresponding first reference data set.

Abstract: A system and method for identifying objects using a machine-vision based system are disclosed. Briefly described, one embodiment is a method that captures a first image of at least one object with an image capture device, processes the first captured image to find an object region based on a reference two-dimensional model and determines a three-dimensional pose estimation based on a reference three-dimensional model that corresponds to the reference two-dimensional model and a runtime three-dimensional representation of the object region where a point-to-point relationship between the reference three-dimensional models of the object and the runtime three-dimensional representation of the object region is not necessarily previously known. Thus, two-dimensional information or data is used to segment an image and three-dimensional information or data used to perform three-dimensional pose estimation on a segment of the image.

Abstract: A system and method for identifying objects using a machine-vision based system are disclosed. Briefly described, one embodiment is a method that captures a first image of at least one object with an image capture device, processes the first captured image to find an object region based on a reference two-dimensional model and determines a three-dimensional pose estimation based on a reference three-dimensional model that corresponds to the reference two-dimensional model and a runtime three-dimensional representation of the object region where a point-to-point relationship between the reference three-dimensional models of the object and the runtime three-dimensional representation of the object region is not necessarily previously known. Thus, two-dimensional information or data is used to segment an image and three-dimensional information or data used to perform three-dimensional pose estimation on a segment of the image.

Abstract: A system and method facilitate machine-vision, for example three-dimensional pose estimation for target objects, using one or more images sensors to acquire images of the target object at one or more positions, and to identify features of the target object in the resulting images. A set of equations is set up exploiting invariant physical relationships between features such as constancy of distances, angles, and areas or volumes enclosed by or between features. The set of equations may be solved to estimate a 3D pose. The number of positions may be determined based on the number of image sensors, number of features identified, and/or number of known physical relationships between less than all features. Knowledge of physical relationships between image sensors and/or between features and image sensors may be employed. A robot path may be transformed based on the pose, to align the path with the target object.