Facet Segmentation and Normal Direction Estimation for a Pile of Objects Grasping System Using Depth-Based Mask R-CNN

Abstract

In the field of robots object segmentation and grasping is a classic problem In order to grasp an object first we need to separate one object from all objects Then we calculate the position and angle of the object to control the robot arm to grasp The method proposed in the past few years is to segment each object then calculate the angle However this method may hit the area of piled objects that is difficult to grip In order to solve the above problems this paper proposes segmentation on each object facet based on deep convolutional neural network (CNN) This ensures one facet of an object is gripped rather than the tip or the area that is hard to grasp We use PCA (Principle Component Analysis) to find the center point principle axis secondary axis and normal direction of an object facet so that we can control the robot arm to grasp In order to train the convolutional neural network we cost 50 hours to collect and label the object facets on each dataset Training the network cost 27 hours We choose to use depth image as the input of the deep convolutional neural network By using depth image the network can learn a facet should contain smooth and continuous depth value change If there is a situation where the depth value is discontinuous or the depth value changes drastically it may be the background or the other facet This method can achieve high-performance object facet segmentation

Date of Award	2019
Original language	English
Supervisor	Shu-Mei Guo (Supervisor)