Abstract: The present disclosure relates to a method and system for calibrating a carton detection system. The method includes receiving a three-dimensional (3D) point cloud of a calibration object, determining a 3D target pose of the calibration object by comparing the 3D point cloud to a point cloud template, receiving a two-dimensional (2D) optical image of the calibration object, identifying one or more markers of the calibration object based on the 2D optical image, determining a marker pose for each of the one or more markers based on the 2D optical image, determining a 2D target pose based on the marker pose for each of the one or more markers, generating a transformation matrix based on the 3D target pose and the 2D target pose, and calibrating the carton detection system based on the transformation matrix.

Abstract: The disclosed embodiments relate to a material handling method that generates three-dimensional (3-D) point cloud data based on a field-of-view of an image capturing unit. A first set of cluster areas are extracted from a plurality of cluster areas based on orientation data of the 3-D point cloud data. Further, a two-dimensional depth map is generated based upon the 3-D point cloud data. A candidate region that corresponds to a cluster area from the first set of cluster areas is determined. A ratio of a cross-sectional area of the cluster area and a cross-sectional area of the container is determined that exceeds a first cross-sectional threshold. Accordingly, a classification score of the candidate region is determined when the determined ratio exceeds a first cross-sectional threshold. In response to classifying the candidate region as a region-of-interest, a navigation path in the container that prevents collision with the region-of-interest is defined.

Abstract: The present disclosure relates to a method and system for calibrating a carton detection system. The method includes receiving a three-dimensional (3D) point cloud of a calibration object, determining a 3D target pose of the calibration object by comparing the 3D point cloud to a point cloud template, receiving a two-dimensional (2D) optical image of the calibration object, identifying one or more markers of the calibration object based on the 2D optical image, determining a marker pose for each of the one or more markers based on the 2D optical image, determining a 2D target pose based on the marker pose for each of the one or more markers, generating a transformation matrix based on the 3D target pose and the 2D target pose, and calibrating the carton detection system based on the transformation matrix.

Abstract: The disclosed embodiments relate to a material handling method that generates three-dimensional (3-D) point cloud data based on a field-of-view of an image capturing unit. A first set of cluster areas are extracted from a plurality of cluster areas based on orientation data of the 3-D point cloud data. Further, a two-dimensional depth map is generated based upon the 3-D point cloud data. A candidate region that corresponds to a cluster area from the first set of cluster areas is determined. A ratio of a cross-sectional area of the cluster area and a cross-sectional area of the container is determined that exceeds a first cross-sectional threshold. Accordingly, a classification score of the candidate region is determined when the determined ratio exceeds a first cross-sectional threshold. In response to classifying the candidate region as a region-of-interest, a navigation path in the container that prevents collision with the region-of-interest is defined.

Abstract: Robotic carton loader or unloader incorporates three-dimensional (3D) and two-dimensional (2D) sensors to detect respectively a 3D point cloud and a 2D image of a carton pile within transportation carrier such as a truck trailer or shipping container. Edge detection is performed using the 3D point cloud, discarding segments that are two small to be part of a product such as a carton. Segments that are too large to correspond to a carton are 2D image processed to detect additional edges. Results from 3D and 2D edge detection are converted in a calibrated 3D space of the material carton loader or unloader to perform one of loading or unloading of the transportation carrier. Image processing can also detect jamming of products sequence from individually controllable zones of a conveyor of the robotic carton loader or unloader for singulated unloading.

Abstract: Robotic carton loader or unloader incorporates three-dimensional (3D) and two-dimensional (2D) sensors to detect respectively a 3D point cloud and a 2D image of a carton pile within transportation carrier such as a truck trailer or shipping container. Edge detection is performed using the 3D point cloud, discarding segments that are two small to be part of a product such as a carton. Segments that are too large to correspond to a carton are 2D image processed to detect additional edges. Results from 3D and 2D edge detection are converted in a calibrated 3D space of the material carton loader or unloader to perform one of loading or unloading of the transportation carrier. Image processing can also detect jamming of products sequence from individually controllable zones of a conveyor of the robotic carton loader or unloader for singulated unloading.

Abstract: Robotic carton loader or unloader incorporates three-dimensional (3D) and two-dimensional (2D) sensors to detect respectively a 3D point cloud and a 2D image of a carton pile within transportation carrier such as a truck trailer or shipping container. Edge detection is performed using the 3D point cloud, discarding segments that are two small to be part of a product such as a carton. Segments that are too large to correspond to a carton are 2D image processed to detect additional edges. Results from 3D and 2D edge detection are converted in a calibrated 3D space of the material carton loader or unloader to perform one of loading or unloading of the transportation carrier. Image processing can also detect jamming of products sequence from individually controllable zones of a conveyor of the robotic carton loader or unloader for singulated unloading.

Abstract: A robotic material handling system has a controller that positions a nose conveyor surface proximate to an article pile, uses a robotic manipulator to robotically move one or more articles per operation onto the nose conveyor from the article pile, and receives a scan of the nose conveyor surface to detect respective locations of any articles received on the nose conveyor surface. The controller determines whether the respective locations of any scanned articles prevent immediate rearward conveyance by the two or more parallel conveyors at a first speed. If so detected, the controller causes at least one of the two or more parallel conveyors to operate at the first speed and at least another one of two or more parallel conveyors to operate at a second speed that is not equal to the first speed so that cartons do not jam or overwhelm a narrower rearward conveyor.

Abstract: Robotic carton loader or unloader incorporates three-dimensional (3D) and two-dimensional (2D) sensors to detect respectively a 3D point cloud and a 2D image of a carton pile within transportation carrier such as a truck trailer or shipping container. Edge detection is performed using the 3D point cloud, discarding segments that are two small to be part of a product such as a carton. Segments that are too large to correspond to a carton are 2D image processed to detect additional edges. Results from 3D and 2D edge detection are converted in a calibrated 3D space of the material carton loader or unloader to perform one of loading or unloading of the transportation carrier. Image processing can also detect jamming of products sequence from individually controllable zones of a conveyor of the robotic carton loader or unloader for singulated unloading.

Abstract: A robotic material handling system has a controller that positions a nose conveyor surface proximate to an article pile, uses a robotic manipulator to robotically move one or more articles per operation onto the nose conveyor from the article pile, and receives a scan of the nose conveyor surface to detect respective locations of any articles received on the nose conveyor surface. The controller determines whether the respective locations of any scanned articles prevent immediate rearward conveyance by the two or more parallel conveyors at a first speed. If so detected, the controller causes at least one of the two or more parallel conveyors to operate at the first speed and at least another one of two or more parallel conveyors to operate at a second speed that is not equal to the first speed so that cartons do not jam or overwhelm a narrower rearward conveyor.

Abstract: Methods, devices, systems, and non-transitory process-readable storage media for a computing device of a robotic carton unloader to identify items to be unloaded from an unloading area within imagery.