Abstract: A method for generating training data can include: determining a set of images; determining a set of masks based on the images; determining a first mesh based on the set of masks; optionally determining a refined mesh by recomputing the first mesh; optionally determining one or more faces of the refined mesh; optionally adding one or more keypoints to the refined mesh; optionally determining a material property set for the object; optionally generating a full object mesh; determining one or more scenes; optionally determining training data based on the one or more scenes; optionally training one or more object detectors using the training data; and detecting one or more objects using the trained object detector.

Abstract: A method for generating training data can include: determining a set of images; determining a set of masks based on the images; determining a first mesh based on the set of masks; optionally determining a refined mesh by recomputing the first mesh; optionally determining one or more faces of the refined mesh; optionally adding one or more keypoints to the refined mesh; optionally determining a material property set for the object; optionally generating a full object mesh; determining one or more scenes; optionally determining training data based on the one or more scenes; optionally training one or more object detectors using the training data; and detecting one or more objects using the trained object detector.

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 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: The disclosed embodiments relates to a method. The method includes defining, by a processor, a first area in a three-dimensional (3D) image of a worksite, including a docked container, based on an identification of one or more sections of the docked container in the 3-D image. The first area is exterior to the docked container. Further, the method includes identifying, by the processor, one or more regions in the first area representative of one or more objects positioned exterior to the docked container. Additionally, the method includes operating, by the processor, a material handling apparatus based on one or more characteristics associated with the one or more objects.

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: 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: 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.