The issues of estimating the poses of simple-shaped objects, such as retail store goods, have been addresses to ease the grasping of objects by robots. Conventional methods to estimate poses with an RGBD camera mounted on robots have difficulty estimating the three-dimensional poses of simple-shaped objects with few shape features. Therefore, in this study, we propose a new class called “poseclass” to indicate the grounding face of an object. The poseclass is of discrete value and solvable as a classification problem; it can be estimated with high accuracy; in addition, the three-dimensional pose estimation problems can be simplified into one-dimensional pose-estimation problem to estimate the yaw angles on the grounding face. We have developed a new neural network (PYNet) to estimate the poseclass and yaw angle, and compared it with conventional methods to determine its ratio of estimating unknown simple-shaped object poses with an angle error of 30° or less. The ratio of PYNet (68.9%) is an 18.1 pt higher than that of the conventional methods (50.8%). Additionally, a PYNet-implemented robot successfully grasped convenience store goods.

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