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JRM Vol.35 No.1 pp. 8-17
doi: 10.20965/jrm.2023.p0008
(2023)

Paper:

PYNet: Poseclass and Yaw Angle Output Network for Object Pose Estimation

Kohei Fujita and Tsuyoshi Tasaki

Meijo University
1-501 Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan

Received:
July 16, 2022
Accepted:
December 7, 2022
Published:
February 20, 2023
Keywords:
object pose estimation, world robot summit, deep neural network
Abstract
PYNet: Poseclass and Yaw Angle Output Network for Object Pose Estimation

PYNet outputs poseclass (grounding face)

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.

Cite this article as:
K. Fujita and T. Tasaki, “PYNet: Poseclass and Yaw Angle Output Network for Object Pose Estimation,” J. Robot. Mechatron., Vol.35, No.1, pp. 8-17, 2023.
Data files:
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Last updated on Mar. 19, 2023