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| 6D Pose Estimation of Sorting Robot Based on Machine Vision |
| ZHAO Lan,SHE Yuan,WEN Xiu-lan,LI Guo-cheng,ZHANG Teng-fei,HE Zhong-le |
| School of Automation, Nanjing Institute of Technology, Nanjing, Jiangsu 211167, China |
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Abstract In order to solve the problems of low degree of automation, poor real-time performance and weak portability of traditional sorting robots when they complete designated actions through manual instruction, an experimental platform of sorting robots based on machine vision is designed, and a high-precision 6D pose estimation method based on the mixed progressive registration strategy of coarse registration and enhanced fine registration is proposed. Firstly, the 3D point cloud information collected by the vision sensor is preprocessed, such as background information removal, target region clipping, ROI extraction, etc., and the sampling consistency initial registration algorithm (SAC-IA) is used for pose rough registration. Then the iterative closest point (ICP) algorithm is used for fine registration and the normal distribution transformation (NDT) is used to enhance the fine registration to achieve a high precision 6D position. The experimental results show that the integration of coarse registration and enhanced fine registration can quickly and accurately obtain the 6D position and attitude of the target to be captured, with errors controlled within 1.5mm and 2 ° compared to the theoretical position and attitude, respectively, and meet the practical needs of sorting robots. The proposed method can be applied to the machine vision based robot assembly, grinding and other occasions with high precision 6D pose estimation.
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Received: 04 May 2023
Published: 27 December 2023
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2023, Vol. 44 
