Abstract: A system and method for determining occlusion are presented. The system receives camera data generated by at least one camera, which includes a first camera having a first camera field of view. The camera data is generated when a stack having a plurality of objects is in the first camera field of view, and describes a stack structure formed from at least an object structure for a first object of the plurality of objects. The system identifies a target feature of or disposed on the object structure, and determines a 2D region that is co-planar with and surrounds the target feature. The system determines a 3D region defined by connecting a location of the first camera and the 2D region. The system determines, based on the camera data and the 3D region, a size of an occluding region, and determines a value of an object recognition confidence parameter.

Abstract: A computing system including a processing circuit in communication with a camera having a field of view. The processing circuit obtains image information based on the objects in the field of view and defines a minimum viable region for a target open corner. Potential minimum viable regions are defined by identifying candidate edges of an object and determining potential intersection points based on the candidate edges. The minimum viable region may then be identified and validated from the potential minimum viable regions.

Abstract: Systems and methods for classifying at least a portion of an image as being textured or textureless are presented. The system receives an image generated by an image capture device, wherein the image represents one or more objects in a field of view of the image capture device. The system generates one or more bitmaps based on at least one image portion of the image. The one or more bitmaps describe whether one or more features for feature detection are present in the at least one image portion, or describe whether one or more visual features for feature detection are present in the at least one image portion, or describe whether there is variation in intensity across the at least one image portion. The system determines whether to classify the at least one image portion as textured or textureless based on the one or more bitmaps.

Abstract: A system and method for determining occlusion are presented. The system receives camera data generated by at least one camera, which includes a first camera having a first camera field of view. The camera data is generated when a stack having a plurality of objects is in the first camera field of view, and describes a stack structure formed from at least an object structure for a first object of the plurality of objects. The system identifies a target feature of or disposed on the object structure, and determines a 2D region that is co-planar with and surrounds the target feature. The system determines a 3D region defined by connecting a location of the first camera and the 2D region. The system determines, based on the camera data and the 3D region, a size of an occluding region, and determines a value of an object recognition confidence parameter.

Abstract: A method and computing system for facilitating edge detection are presented. The computing system may include at least one processing circuit configured to receive image information representing a group of objects in a camera field of view, and to identify, from the image information, a plurality of candidate edges associated with the group of objects. The at least one processing circuit may further determine, when the plurality of candidate edges include a first candidate edge which is formed based on a border between a first image region and a second image region, whether the image information satisfies a defined darkness condition at the first candidate edge. The at least one processing circuit may further select a subset of the plurality of candidate edges to form a selected subset of candidate edges for representing the physical edges of the group of objects.

Abstract: A method and computing system for performing the method are presented. The method may include receiving image information representing an object; identifying a set of one or more matching object recognition templates associated with a set of one or more detection hypotheses. The method may further include selecting a primary detection hypothesis associated with a matching object recognition template; generating a primary candidate region based on the matching object recognition template; determining at least one of: (i) whether the set of one or more matching object recognition templates has a subset of one or more remaining matching templates, or (ii) whether the image information has a portion representing an unmatched region; and generating a safety volume list based on at least one of: (i) the unmatched region, or (ii) one or more additional candidate regions that are generated based on the subset of one or more remaining matching templates.

Abstract: A method performed by a computing system is presented. The method may include the computing system receiving image information that represents an object surface associated with a flexible object, and identifying, as a grip region, a surface region of the object surface that satisfies a defined smoothness condition and has a region size that is larger than or equal to a defined region size threshold, wherein the grip region is identified based on the image information. The method may further include identifying, as a safety region, a three-dimensional (3D) region which surrounds the grip region in one or more horizontal dimensions, and which extends from the grip region along a vertical dimension that is perpendicular to the one or more horizontal dimensions. The method may further include performing robot motion planning based on the grip region and the safety region.

Abstract: A system and method for operating a robotic system to scan and register unrecognized objects is disclosed. The robotic system may use an image data representative of an unrecognized object located at a start location to implement operations for transferring the unrecognized object from the start location. While implementing the operations, the robotic system may obtain additional data, including scanning results of one or more portions of the unrecognized object not included in the image data. The robotic system may use the additional data to register the unrecognized object.

Abstract: The present disclosure relates to verifying an initial object estimation of an object. A two-dimensional (2D) image representative of an environment including one or more objects may be obtained. The 2D image may be inspected to detect edges of an object. The edges may be processed to verify or update an initial object estimation to increase the accuracy of an object detection result.

Abstract: The present disclosure relates to detecting and registering unrecognized or unregistered objects. A minimum viable range (MVR) may be derived based on inspecting image data that represents objects. The MVR may be determined to be a certain MVR or an uncertain MVR according to one or more features represented in the image data. The MVR may be used to register corresponding objects according to the certain or uncertain determination.

Abstract: A system and method for determining occlusion are presented. The system receives camera data generated by at least one camera, which includes a first camera having a first camera field of view. The camera data is generated when a stack having a plurality of objects is in the first camera field of view, and describes a stack structure formed from at least an object structure for a first object of the plurality of objects. The system identifies a target feature of or disposed on the object structure, and determines a 2D region that is co-planar with and surrounds the target feature. The system determines a 3D region defined by connecting a location of the first camera and the 2D region. The system determines, based on the camera data and the 3D region, a size of an occluding region, and determines a value of an object recognition confidence parameter.

Abstract: A method performed by a computing system is presented. The method may include the computing system receiving image information that represents an object surface associated with a flexible object, and identifying, as a grip region, a surface region of the object surface that satisfies a defined smoothness condition and has a region size that is larger than or equal to a defined region size threshold, wherein the grip region is identified based on the image information. The method may further include identifying, as a safety region, a three-dimensional (3D) region which surrounds the grip region in one or more horizontal dimensions, and which extends from the grip region along a vertical dimension that is perpendicular to the one or more horizontal dimensions. The method may further include performing robot motion planning based on the grip region and the safety region.

Abstract: A method and computing system for performing the method are presented. The method may include receiving image information representing an object; identifying a set of one or more matching object recognition templates associated with a set of one or more detection hypotheses. The method may further include selecting a primary detection hypothesis associated with a matching object recognition template; generating a primary candidate region based on the matching object recognition template; determining at least one of: (i) whether the set of one or more matching object recognition templates has a subset of one or more remaining matching templates, or (ii) whether the image information has a portion representing an unmatched region; and generating a safety volume list based on at least one of: (i) the unmatched region, or (ii) one or more additional candidate regions that are generated based on the subset of one or more remaining matching templates.

Abstract: The present disclosure relates to methods and systems for generating a verified minimum viable range (MVR) of an object. An exposed outer corner and exposed edges of an object may be identified by processing one or more image data. An initial MVR may be generated by identifying opposing parallel edges opposing the exposed edges. The initial MVR may be adjusted, and the adjusted result may be tested to generate a verified MVR.

Abstract: A method of operating a robotic system includes: receiving first image data representative of a package surface; identifying a pair of edges based on the first image data; determining a minimum viable region based on the pair of edges; receiving second image data representative of the package after the lift; and creating registration data based on the first and second image data.

Abstract: The present disclosure relates to verifying an initial object estimation of an object. A two-dimensional (2D) image representative of an environment including one or more objects may be obtained. The 2D image may be inspected to detect edges of an object. The edges may be processed to verify or update an initial object estimation to increase the accuracy of an object detection result.

Abstract: Systems and methods for processing spatial structure data are provided. The system accesses spatial structure data, which describes object structure, and which has depth information indicative of a plurality of layers for the object structure. The system further extracts, from the spatial structure data, a portion of the spatial structure data representative of one layer of the plurality of layers. The system identifies, from the portion of the spatial structure data, a plurality of vertices that describe a contour of the layer. Additionally, the system identifies convex corners of the layer based on the plurality of vertices and performs object recognition according to the convex corners.

Abstract: A computing system and method for object recognition is presented. The method includes the computing system obtaining an image for representing the one or more objects, and generating a target image portion associated with one of the one or more objects. The computing system determines whether to classify the target image portion as textured or textureless, and selects a template storage space from among a first and second template storage space, wherein the first template storage space is cleared more often relative to the second template storage space. The first template storage space is selected in response to a textureless classification, and the second template storage space is selected as the template storage space in response to a textured classification. The computing system performs object recognition based on the target image portion and the selected template storage space.

Abstract: A method of operating a robotic system includes: receiving first image data representative of a package surface; identifying a pair of edges based on the first image data; determining a minimum viable region based on the pair of edges; receiving second image data representative of the package after the lift; and creating registration data based on the first and second image data.

Abstract: A computing system and method for object recognition is presented. The method includes the computing system obtaining an image for representing the one or more objects, and generating a target image portion associated with one of the one or more objects. The computing system determines whether to classify the target image portion as textured or textureless, and selects a template storage space from among a first and second template storage space, wherein the first template storage space is cleared more often relative to the second template storage space. The first template storage space is selected in response to a textureless classification, and the second template storage space is selected as the template storage space in response to a textured classification. The computing system performs object recognition based on the target image portion and the selected template storage space.