Robot autonomous classification grabbing method based on YOLOv3

Abstract

The invention discloses a robot autonomous classification grabbing method based on YOLOv3, and the method is characterized in that the method comprises: collecting and constructing a target object sample data set; training the YOLOv3 target detection network to obtain a target object recognition model; acquiring a color image and a depth image of the target object; processing the color image by using the trained YOLOv3 target detection network to obtain the category information and the position information of the target object to be grabbed, and further processing by combining the depth imageto obtain the point cloud information of the target object; and performing minimum bounding box solving on the point cloud information, calculating the main direction of the point cloud by combining aPCA algorithm, calibrating the coordinate data of the X, Y and Z axes of the target object, and calculating the six-degree-of-freedom pose of the target object relative to the robot coordinate system. According to the method, the YOLOv3 algorithm is adopted, the object grabbing pose is estimated through point cloud preprocessing, PCA and other methods, and then the robot grabs the target objectsin a classified mode.

Description

technical field [0001] The invention relates to a robot autonomous classification and grasping method, in particular to a robot autonomous classification and grasping method based on YOLOv3. Background technique [0002] At present, my country's aging population is serious and there is a shortage of labor force. The demand for service robots is also increasing. However, the unstructured environment in which service robots work has also brought many technical problems. One of the most important problems is the unstructured environment. Autonomous grasping by robots in environments. Grasping, as one of the main ways for robots to interact with the real world, is an urgent problem to be solved. Different from the grasping of workpieces by industrial robots in a structured environment, the automatic grasping of service robots in an unstructured environment faces many challenges, such as dynamic environments, lighting changes, mutual occlusion between objects, and the most import...

AI Technical Summary

Problems solved by technology

However, this algorithm is easily affected by the working environment, sensitive to lighting, object shape, size, etc., has poor robustness, and weak generalization ability.

Images