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: A system and method for processing candidate edges are presented. The method may be performed when spatial structure information and 2D image information are stored. The method may include identifying, based on the spatial structure information, a 3D location that represents a corner of an object structure; identifying, based on the spatial structure information, a 3D vector that extends from the 3D location and is parallel with an edge; determining a 2D location within the 2D image information that corresponds to the 3D location; determining a 2D vector within the 2D image information that corresponds to the 3D vector; determining an edge detection region; identifying a cluster of candidate edges within the edge detection region that does not represent any object edge; identifying, as a 2D object edge, a candidate edge that is not part of the identified cluster; and performing segmentation based on the 2D object edge.

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 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: 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: 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: 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 robotic system includes: a control unit configured to: receive an object set including one or more object entries, wherein: the object entries correspond to source objects of an object source, each of the object entries are described by one or more object entry properties; receive sensor information representing one or more detectable object properties for detectable source objects of the object source; calculate an object match probability between the detectable source objects and the object entries based on a property correlation between the detectable object properties of the detectable source objects and the object entry properties of the object entries; generate an object identity approximation for each of the detectable source objects based on a comparison between the object match probability for each of the detectable source objects corresponding to a particular instance of the object entries; select a target object from the detectable source objects; generate an object handling strategy, for impleme

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: 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: A system and method of detecting objects are provided. The method includes generating first edge information from first image data representing an object based on a first mode of image capture, generating second edge information from second image data representing the object based on a second mode of image capture, the second mode being different from the first mode, fusing the first edge information with the second edge information to generate fused edge information, generating an object detection hypothesis based on the fused edge information, and validating the object detection hypothesis based on the fused edge information, the first edge information, and/or the second edge information.

Abstract: A robotic system includes: a control unit configured to: receive an object set including one or more object entries, wherein: the object entries correspond to source objects of an object source, each of the object entries are described by one or more object entry properties; receive sensor information representing one or more detectable object properties for detectable source objects of the object source; calculate an object match probability between the detectable source objects and the object entries based on a property correlation between the detectable object properties of the detectable source objects and the object entry properties of the object entries; generate an object identity approximation for each of the detectable source objects based on a comparison between the object match probability for each of the detectable source objects corresponding to a particular instance of the object entries; select a target object from the detectable source objects; generate an object handling strategy, for impleme

Abstract: A method for operating a robotic system includes obtaining and processing first data representative of an object at a start location. An event may be detected while implementing an operation based on the image data. A gripper height that corresponds to the event may be determined. Accordingly, the method may include calculating an object height that represents a height estimate of the object.

Abstract: A method for operating a robotic system includes obtaining and processing first data representative of an object at a start location; obtaining additional data as the object is transferred to a task location, wherein the additional data includes information regarding one or more edges and/or one or more surfaces separate from portions of the object captured in the first data; and creating registration data representative of the object based on the additional data.

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 detecting and registering unrecognized or unregistered objects. A minimum viable range (MVR) may be derived based on inspecting image data that represents objects at a start location. 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: 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 for operating a robotic system includes obtaining and processing first data representative of an object at a start location; obtaining additional data as the object is transferred to a task location, wherein the additional data includes information regarding one or more edges and/or one or more surfaces separate from portions of the object captured in the first data; and creating registration data representative of the object based on the additional data.