Recognition and Pose Estimation of Auto Parts for an Autonomous Spray Painting Robot

作     者：Lin, Weiyang Anwar, Ali Li, Zhan Tong, Mingsi Qiu, Jianbin Gao, Huijun 

作者机构：Harbin Inst Technol State Key Lab Robot & Syst Key Lab Microsyst & Microstruct Mfg Minist Educ Harbin 150001 Heilongjiang Peoples R China Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150001 Heilongjiang Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS》 (IEEE产业信息学汇刊)

年 卷 期：2019年第15卷第3期

页      面：1709-1719页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61627901, 61873311, 61503101] Key Laboratory of Micro-Systems and Micro-Structures Manufacturing of Ministry of Education (HIT) [2017 KM008] Self-Planned Task of State Key Laboratory of Robotics and System (HIT) [SKLRS201809B] 111 Project [B16014] 

主　　题：Autonomous robotics principal components analysis spray painting robot three-dimensional object recognition visual servoing 

摘      要：The autonomous operation of industrial robots with minimal human supervision has always been in high demand. To prepare the autonomous operation of a car part spray painting robot, novel object detection, and pose estimation algorithms have been developed in this paper. The object detection part used principal components analysis (PCA) to reduce the dimension of three-dimensional (3-D) point cloud to 2-D binary image. Distance measure between the auto and cross correlation of the binary features was established to find out the similarity between them. Resultantly, the type of auto part was successfully obtained. Furthermore, iterative closest point (ICP) algorithm was used to estimate the pose difference of the auto part with respect to the camera reference frame, which was mounted on the robot. An issue with ICP s lack of robustness to local minimum was solved by the combination of ICP and genetic algorithm (GA). This allowed the optimization of pose error and addressed the problem of local minimum entrapment in ICP. For experimental validation: the proposed object recognition pipeline was implemented in both serial and parallel programming paradigms. The results were obtained for the acquired point clouds of side body car parts and compared with the major 3-D object detection systems in terms of computational cost. Pose estimation error was calculated with both ICP and the modified point set registration schemes, and it was shown to be decreasing in the case of later. All shown results supported the research claims.