Abstract: In order to realize the identification of stacked parts, the disorderly grasping and the detection of the assembly status of parts, relying on the intelligent transformation project of a pump production line, on the basis of the existing pump production line, 
a set of disorderly capture system of stacked parts and components based on 3D machine vision has been established,  thus realizing the automation of water pump production. Firstly, the global feature descriptor PPF algorithm was used to extract parts features. Then, in the off-line training stage, the visible points that met the conditions were combined in pairs, and the point pair features were calculated to obtain the model describing the global information of the object; in the online matching stage, parts were identified by voting strategy based on Hough transform. Furthermore, RANSAC algorithm was used for rough estimate of position and pose, and ICP algorithm was used to fine-tune the pose results to obtain the optimal pose estimation. The calibrated transformation matrix determined the position and attitude of the parts in the real-world coordinate system to guide the manipulator to grasp and place the stacked parts accurately. Finally, the images of parts under test state were compared with those of correctly assembled parts to judge the assembly state of parts. Through the water pump production line transformation project, a disordered grabbing system was built for verification. The result of research shows that: taking the pump body as an example, the number of selected point pairs is 730 830, the matching accuracy can reach 94.64%, and the matching time consumption is 1.142 4 s. The average success rate of the overall capture is 92.3%, and the time consumption of recognition, rough estimation of position and pose and precision matching all meet the real-time requirements, which is practical.

Key words:  pump production line; component assembly; stack parts; optimal pose estimation; disorderly capture; assembly condition detection