3D-RPP: a novel 3D vision-based Pose Perception Approach for Industrial Robots

Abstract

Enhanced by artificial intelligence, industrial robots are becoming more powerful, gaining a large variety of applications in intelligent factories. Pose perception, the aim of which is to obtain the joint coordinates of the robot in the camera coordinate system, has been proposed as a promising technology for multi-robot intelligent interaction. A large number of efforts have been made on studying robot pose perception. However, most of existing studies estimate the position of robot joints in 3D space by just using 2D color images, and perform pose perception by estimating the position of joint key points in the image, which could become infeasible in scenarios with various camera views and background environments. To address the issue, considering the information about the robot itself as a prior knowledge, we propose a novel approach for robot pose perception, named 3D-RPP. We adopt a 3D visual point cloud to estimate the rigid transformation of the camera coordinate system with respect to the robot base coordinate system, which effectively improves the accuracy of the obtained robot joint position in the camera coordinate system. We conduct extensive experiments on ROKAE xMate3 robot to investigate the performance of 3D-RPP, and the experimental results show that 3D-RPP could solve the pose perception problem well.