A pose recognition and picking method for stacked workpieces based on rgbd camera

Abstract

The invention relates to a stacked workpiece posture recognizing and picking method based on an RGBD camera. The method includes the following steps that 1), parameters in the RGBD camera are calibrated; 2), training is carried out according to a pre-obtained 3D model of a to-be-captured workpiece, and a 2D model for matching is generated; 3), an RGB image and a depth image of the to-be-recognizedworkpiece are obtained through the RGBD camera, and contour information of the to-be-captured workpiece is obtained; 4), two-dimensional position information of the to-be-captured workpiece in an image pixel coordinate system and a six-freedom-degree pose under a camera coordinate system are obtained; 5), a six-freedom-degree pose of the to-be-captured workpiece under a robot coordinate system isobtained; and 6), a six-axis robot is controlled to pick the to-be-captured workpiece. Compared with the prior art, the RGBD camera with low cost is utilized, in combination with RGB and depth information, the postures of various types of workpieces stacked disorderly are recognized and captured, and the method is high in precision, low in cost and high in adaptability, and can meet the requirements of industrial production.

Description

technical field [0001] The invention relates to the field of intelligent robots, in particular to a gesture recognition and picking method for stacked workpieces based on an RGBD camera. Background technique [0002] With the rapid development of robotics and artificial intelligence technology, industrial assembly lines are also increasingly moving towards automation. The foreign industry 4.0 strategy and my country's "Made in China 2025" have put forward the application of robotics and artificial intelligence technology in the industrial field. high demands. [0003] In some industrial production lines, we need to sort and pick up disorderly stacked industrial parts of various shapes. Traditional industrial production lines rely on manpower for sorting with low efficiency and high labor costs. More and more Enterprises hope to use robots to replace manpower and realize the classification and picking of industrial parts. [0004] At present, there are some patents for workp...

AI Technical Summary

Problems solved by technology

The industrial application prospects of such methods are limited. In the case of cluttered background or stacked workpieces, it is difficult to identify workpieces in the field of view through traditional image processing methods such as edge detection and template detection; at the same time, edge detection There are some parameters that need to be manually adjusted in the algorithm, and it is often difficult to adapt to different grasping objects, that is, different shapes, different sizes, or even mixed types of workpieces.

[0007] There is also a solution for workpiece grasping based on binocular vision, which overcomes the disadvantages of monocular vision to a certain extent by using the solution of binocular vision, and can estimate the pose of the workpiece to be grasped in three-dimensional space. The essence of the solution based on binocular vision is still to perform template matching in a single monocular vision picture, and then fuse the information of the binocular camera, which still cannot solve the problem of chaotic stacking of artifacts.

In addition, the binocular vision method requires binocular positioning, and the error of binocular positioning has a great impact on the error of final workpiece recognition and pose estimation.

[0008] To sum up, the traditional solutions are either expensive, or can only obtain the two-dimensional plane attitude of the workpiece, or the accuracy and robustness of the algorithm are not strong, and cannot adapt to complex industrial application scenarios, and cannot guarantee the accuracy of various The shape and various types of workpieces can accurately identify and calculate the pose, which cannot meet the needs of industrial production

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