SYSTEM AND METHOD FOR LOCATING A THREE-DIMENSIONAL OBJECT USING MACHINE VISION
First Claim
1. A method for registering an object in three dimensions using machine vision comprising the steps of:
- at training time, acquiring training images of an object used for training with one or more cameras;
at runtime, acquiring runtime images of an object to be registered at runtime with the one or more cameras; and
determining a three dimensional pose transformation between the pose of the object used training time and the pose of the object to be registered at runtime by(a) defining features in each of the runtime images as three-dimensional rays through an origin of each of the one or more camera'"'"'s, respectively,(b) matching the three-dimensional rays to corresponding runtime features from the training images, and(c) computing an optimal pose estimate which maps the training features onto the corresponding three-dimensional rays of runtime features using iterative, reweighted least squares analysis.
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Abstract
This invention provides a system and method for determining position of a viewed object in three dimensions by employing 2D machine vision processes on each of a plurality of planar faces of the object, and thereby refining the location of the object. First a rough pose estimate of the object is derived. This rough pose estimate can be based upon predetermined pose data, or can be derived by acquiring a plurality of planar face poses of the object (using, for example multiple cameras) and correlating the corners of the trained image pattern, which have known coordinates relative to the origin, to the acquired patterns. Once the rough pose is achieved, this is refined by defining the pose as a quaternion (a, b, c and d) for rotation and a three variables (x, y, z) for translation and employing an iterative weighted, least squares error calculation to minimize the error between the edgelets of trained model image and the acquired runtime edgelets. The overall, refined/optimized pose estimate incorporates data from each of the cameras'"'"' acquired images. Thereby, the estimate minimizes the total error between the edgelets of each camera'"'"'s/view'"'"'s trained model image and the associated camera'"'"'s/view'"'"'s acquired runtime edgelets. A final transformation of trained features relative to the runtime features is derived from the iterative error computation.
139 Citations
13 Claims
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1. A method for registering an object in three dimensions using machine vision comprising the steps of:
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at training time, acquiring training images of an object used for training with one or more cameras; at runtime, acquiring runtime images of an object to be registered at runtime with the one or more cameras; and determining a three dimensional pose transformation between the pose of the object used training time and the pose of the object to be registered at runtime by (a) defining features in each of the runtime images as three-dimensional rays through an origin of each of the one or more camera'"'"'s, respectively, (b) matching the three-dimensional rays to corresponding runtime features from the training images, and (c) computing an optimal pose estimate which maps the training features onto the corresponding three-dimensional rays of runtime features using iterative, reweighted least squares analysis. - View Dependent Claims (2, 3, 4, 5, 6, 9, 10, 11, 12)
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7. A system for registering an object in three dimensions using machine vision comprising:
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one or more cameras constructed and arranged so that, at training time, the one or more cameras each acquire training images of an object used for training, and at runtime, the one or more cameras acquire runtime images of an object to be registered at runtime with the one or more cameras; and a pose transformation determination process that computes a three-dimensional pose transformation between the pose of the object used training time and the pose of the object to be registered at runtime by (a) defining features in each of the runtime images as three-dimensional rays through an origin of each of the one or more camera'"'"'s, respectively, (b) matching the three-dimensional rays to corresponding runtime features from the training images, and (c) computing an optimal pose estimate which maps the training features onto the corresponding three-dimensional rays of runtime features using iterative, reweighted least squares analysis. - View Dependent Claims (8)
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13. A computer-readable medium containing executable program instructions, which when executed by the computer perform the steps of registering an object in three dimensions using machine vision, the executable program instructions comprising program instructions for:
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at training time, acquiring training images of an object used for training with one or more cameras; at runtime, acquiring runtime images of an object to be registered at runtime with the one or more cameras; and determining a three dimensional pose transformation between the pose of the object used training time and the pose of the object to be registered at runtime by (a) defining features in each of the runtime images as three-dimensional rays through an origin of each of the one or more camera'"'"'s, respectively, (b) matching the three-dimensional rays to corresponding runtime features from the training images, and (c) computing an optimal pose estimate which maps the training features onto the corresponding three-dimensional rays of runtime features using iterative, reweighted least squares analysis.
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Specification